Update from the development fork

README.md

@@ -1,4 +1,9 @@
-# ![nf-core/pathogensurveillance](docs/images/nf-core-pathogensurveillance_logo_light.png#gh-light-mode-only) ![nf-core/pathogensurveillance](docs/images/nf-core-pathogensurveillance_logo_dark.png#gh-dark-mode-only)
+<picture>
+  <source media="(prefers-color-scheme: dark)" srcset="docs/images/nf-core-pathogensurveillance_logo_dark.png">
+  <source media="(prefers-color-scheme: light)" srcset="docs/images/nf-core-pathogensurveillance_logo_light.png">
+  <img alt="nf-core/pathogensurveillance" src="docs/images/nf-core-pathogensurveillance_logo_light.png">
+</picture>
+
 
 [![AWS CI](https://img.shields.io/badge/CI%20tests-full%20size-FF9900?labelColor=000000&logo=Amazon%20AWS)](https://nf-co.re/pathogensurveillance/results)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.XXXXXXX-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.XXXXXXX)
 
@@ -17,14 +22,18 @@
 <!-- TODO nf-core: Write a 1-2 sentence summary of what data the pipeline is for and what it does -->
 
 **nf-core/pathogensurveillance** is a population genomic pipeline for pathogen diagnosis, variant detection, and biosurveillance.
-The pipeline accepts the paths to raw reads for one or more organisms and creates reports in the form of interactive HTML reports or PDF documents.
-Significant features include the ability to analyze unidentified eukaryotic and prokaryotic samples, creation of reports for multiple user-defined groupings of samples, automated discovery and downloading of reference assemblies from NCBI RefSeq, and rapid initial identification based on k-mer sketches followed by a more robust core genome phylogeny.
+The pipeline accepts the paths to raw reads for one or more organisms (in the form of a CSV file) and creates reports in the form of interactive HTML reports or PDF documents.
+Significant features include the ability to analyze unidentified eukaryotic and prokaryotic samples, creation of reports for multiple user-defined groupings of samples, automated discovery and downloading of reference assemblies from NCBI RefSeq, and rapid initial identification based on k-mer sketches followed by a more robust core genome phylogeny and SNP-based phylogeny.
 
-The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from [nf-core/modules](https://github.com/nf-core/modules) in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!
+The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner.
+It uses Docker/Singularity containers making installation trivial and results highly reproducible.
+The [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies.
+Where possible, these processes have been submitted to and installed from [nf-core/modules](https://github.com/nf-core/modules) in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!
 
 <!-- TODO nf-core: Add full-sized test dataset and amend the paragraph below if applicable -->
 
-On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world data sets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources.The results obtained from the full-sized test can be viewed on the [nf-core website](https://nf-co.re/pathogensurveillance/results).
+On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure.
+This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world data sets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources.The results obtained from the full-sized test can be viewed on the [nf-core website](https://nf-co.re/pathogensurveillance/results).
 
 ## Pipeline summary
 
@@ -39,7 +48,7 @@ On release, automated continuous integration tests run the pipeline on a full-si
 3. Download the pipeline and test it on a minimal dataset with a single command:
 
    ```bash
-   nextflow run nf-core/pathogensurveillance -profile test,YOURPROFILE --outdir <OUTDIR>
+   nextflow run nf-core/pathogensurveillance -profile test,YOURPROFILE --outdir <OUTDIR> -resume
    ```
 
    Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (`YOURPROFILE` in the example command above). You can chain multiple config profiles in a comma-separated string.
@@ -54,9 +63,17 @@ On release, automated continuous integration tests run the pipeline on a full-si
    <!-- TODO nf-core: Update the example "typical command" below used to run the pipeline -->
 
    ```bash
-   nextflow run nf-core/pathogensurveillance --input samplesheet.csv --outdir <OUTDIR> --genome GRCh37 -profile <docker/singularity/podman/shifter/charliecloud/conda/institute>
+   nextflow run nf-core/pathogensurveillance --input samplesheet.csv --outdir <OUTDIR> -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> -resume
    ```
 
+You can also try running a small example dataset hosted with the source code using the following command (no need to download anything):
+
+```
+nextflow run nf-core/pathogensurveillance --input https://raw.githubusercontent.com/grunwaldlab/pathogensurveillance/master/test/data/metadata_small.csv --outdir test_out --download_bakta_db true -profile docker -resume
+```
+
+
+
 ## Documentation
 
 The nf-core/pathogensurveillance pipeline comes with documentation about the pipeline [usage](https://nf-co.re/pathogensurveillance/usage), [parameters](https://nf-co.re/pathogensurveillance/parameters) and [output](https://nf-co.re/pathogensurveillance/output).

assets/main_report/.gitignore

@@ -1,3 +1,6 @@
+/.quarto/
 _book
-_bookdown_files
-_main_files
+_site
+*_files
+*.html
+*.ipynb

assets/main_report/_extensions/quarto-ext/fontawesome/_extension.yml

@@ -0,0 +1,7 @@
+title: Font Awesome support
+author: Carlos Scheidegger
+version: 1.1.0
+quarto-required: ">=1.2.269"
+contributes:
+  shortcodes:
+    - fontawesome.lua