From 330539a723d15cb2a6ca909be11f14cc4e288322 Mon Sep 17 00:00:00 2001 From: stephenturner Date: Thu, 22 Aug 2024 08:36:20 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20stephent?= =?UTF-8?q?urner/biorecap@161f4dd2db9c8a5a2cd6c8baae279764ffb753ec=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 404.html | 10 ++++++++-- CONTRIBUTING.html | 5 ++--- LICENSE-text.html | 5 ++--- LICENSE.html | 5 ++--- apple-touch-icon-120x120.png | Bin 0 -> 10037 bytes apple-touch-icon-152x152.png | Bin 0 -> 13628 bytes apple-touch-icon-180x180.png | Bin 0 -> 16805 bytes apple-touch-icon-60x60.png | Bin 0 -> 4297 bytes apple-touch-icon-76x76.png | Bin 0 -> 5569 bytes apple-touch-icon.png | Bin 0 -> 16805 bytes authors.html | 5 ++--- favicon-16x16.png | Bin 0 -> 1195 bytes favicon-32x32.png | Bin 0 -> 2111 bytes favicon.ico | Bin 0 -> 15086 bytes index.html | 16 ++++++++++++++-- logo.png | Bin 0 -> 39473 bytes pkgdown.yml | 2 +- reference/add_prompt.html | 25 ++++++++++++------------- reference/add_prompt_subject.html | 5 ++--- reference/add_summary.html | 5 ++--- reference/biorecap-package.html | 10 ++++++---- reference/biorecap_report.html | 5 ++--- reference/build_prompt_preprint.html | 5 ++--- reference/build_prompt_subject.html | 5 ++--- reference/example_preprints.html | 5 ++--- reference/figures/logo.drawio | 19 +++++++++++++++++++ reference/figures/logo.png | Bin 0 -> 39473 bytes reference/get_preprints.html | 25 ++++++++++++------------- reference/index.html | 5 ++--- reference/reexports.html | 7 +++---- reference/subjects.html | 5 ++--- reference/tt_preprints.html | 5 ++--- search.json | 2 +- 33 files changed, 102 insertions(+), 79 deletions(-) create mode 100644 apple-touch-icon-120x120.png create mode 100644 apple-touch-icon-152x152.png create mode 100644 apple-touch-icon-180x180.png create mode 100644 apple-touch-icon-60x60.png create mode 100644 apple-touch-icon-76x76.png create mode 100644 apple-touch-icon.png create mode 100644 favicon-16x16.png create mode 100644 favicon-32x32.png create mode 100644 favicon.ico create mode 100644 logo.png create mode 100644 reference/figures/logo.drawio create mode 100644 reference/figures/logo.png diff --git a/404.html b/404.html index 8350653..13e4761 100644 --- a/404.html +++ b/404.html @@ -6,11 +6,18 @@ Page not found (404) • biorecap + + + + + + + Skip to contents @@ -44,8 +51,7 @@
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diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html index ca02244..937bc0a 100644 --- a/CONTRIBUTING.html +++ b/CONTRIBUTING.html @@ -1,5 +1,5 @@ -Contributing to biorecap • biorecap +Contributing to biorecap • biorecap Skip to contents @@ -26,8 +26,7 @@
- -

Contributing to biorecap

+

Contributing to biorecap

Source: .github/CONTRIBUTING.md
diff --git a/LICENSE-text.html b/LICENSE-text.html index 1d80f37..02aa11d 100644 --- a/LICENSE-text.html +++ b/LICENSE-text.html @@ -1,5 +1,5 @@ -License • biorecap +License • biorecap Skip to contents @@ -26,8 +26,7 @@
- -

License

+

License

diff --git a/LICENSE.html b/LICENSE.html index b737bb6..e8c1414 100644 --- a/LICENSE.html +++ b/LICENSE.html @@ -1,5 +1,5 @@ -MIT License • biorecap +MIT License • biorecap Skip to contents @@ -26,8 +26,7 @@
- -

MIT License

+

MIT License

Source: LICENSE.md
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- -

Authors and Citation

+

Authors and Citation

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z#W>y)&*s^bG@c*%jCj|bENks*cE>+%>%sFfc08wZR`$&$?2nt{FQ<71XzlHtIzG%o zby@3LF-9|d-pq-ijKu~$^nlG!mNWBe*>DxH(Q;~KF|l7?YQtjIUK`0b@~xDSvi}7z C_?XWC literal 0 HcmV?d00001 diff --git a/index.html b/index.html index bd2900e..13787d3 100644 --- a/index.html +++ b/index.html @@ -6,6 +6,12 @@ Retrieve and summarize bioRxiv preprints with a local LLM using ollama • biorecap + + + + + + @@ -13,6 +19,7 @@ + Skip to contents @@ -46,11 +53,15 @@
-

biorecap -

+
+

biorecap + +

+

Retrieve and summarize bioRxiv preprints using a local LLM with Ollama via ollamar.

+

Turner, S. D. (2024). biorecap: an R package for summarizing bioRxiv preprints with a local LLM. arXiv, 2408.11707. https://doi.org/10.48550/arXiv.2408.11707.

Installation

@@ -253,6 +264,7 @@

Developers

Dev status

  • R-CMD-check
    • +
  • arXiv
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zAkqA*vXQ!;7qk@AIOKlneN?7YZ@%$+` z^)D3Q3A)_bau%rMraaT1p~lk`NYuU)RuiZ(9f0)3@z=VB&H!dHF4n9*Faq3&l!&b? zqt(%uG*I+j-&O9?HVx*uS!VPEtJ+oaY{ZwFS^x>%g-{=5@g=!}GLjH7dDQO{NUoM$mh@$|#^) -Add prompt to a data frame of preprints — add_prompt • biorecap +Add prompt to a data frame of preprints — add_prompt • biorecap Skip to contents @@ -26,8 +26,7 @@
- -

Add prompt to a data frame of preprints

+

Add prompt to a data frame of preprints

Source: R/biorecap.R
add_prompt.Rd
@@ -70,16 +69,16 @@

Examples#> # A tibble: 60 × 5 #> subject title url abstract prompt #> <chr> <chr> <chr> <chr> <chr> -#> 1 bioinformatics Tesorai Search: Large pretrained model … http… Current… "I am… -#> 2 bioinformatics DIAMOND2GO: A rapid Gene Ontology assig… http… DIAMOND… "I am… -#> 3 bioinformatics Multi-Modal Large Language Model Enable… http… Predict… "I am… -#> 4 bioinformatics Sawfish: Improving long-read structural… http… Motivat… "I am… -#> 5 bioinformatics Towards Digital Quantification of Ploid… http… Abnorma… "I am… -#> 6 bioinformatics Interdependent regulation of alternativ… http… Alterna… "I am… -#> 7 bioinformatics UnigeneFinder: An automated pipeline fo… http… For mos… "I am… -#> 8 bioinformatics Normalization of Single-cell RNA-seq Da… http… Normali… "I am… -#> 9 bioinformatics Phytochemical Analysis and Cytotoxic Ef… http… Hepatoc… "I am… -#> 10 bioinformatics mobileRNA: a tool for efficient analysi… http… In plan… "I am… +#> 1 bioinformatics Unsupervised domain classification of A… http… The rel… "I am… +#> 2 bioinformatics Testing and overcoming the limitations … http… Modular… "I am… +#> 3 bioinformatics Decoding multicellular niche formation … http… Accurat… "I am… +#> 4 bioinformatics Insights from Molecular Docking and Dyn… http… Alpha-s… "I am… +#> 5 bioinformatics TUSCAN: Tumor segmentation and classifi… http… The ide… "I am… +#> 6 bioinformatics Characterizing the role of exosomal miR… http… Backgro… "I am… +#> 7 bioinformatics The Lomb-Scargle periodogram-based diff… http… Motivat… "I am… +#> 8 bioinformatics Protein stability models fail to captur… http… There i… "I am… +#> 9 bioinformatics CryptoBench: Cryptic protein-ligand bin… http… Structu… "I am… +#> 10 bioinformatics haCCA: Multi-module Integrating of spat… http… Spatial… "I am… #> # ℹ 50 more rows

diff --git a/reference/add_prompt_subject.html b/reference/add_prompt_subject.html index 4ef7ae8..24a9d53 100644 --- a/reference/add_prompt_subject.html +++ b/reference/add_prompt_subject.html @@ -1,5 +1,5 @@ -Add prompts for an entire subject — add_prompt_subject • biorecap +Add prompts for an entire subject — add_prompt_subject • biorecap Skip to contents @@ -26,8 +26,7 @@
- -

Add prompts for an entire subject

+

Add prompts for an entire subject

Source: R/biorecap.R
add_prompt_subject.Rd
diff --git a/reference/add_summary.html b/reference/add_summary.html index c4b2b75..d82ee9d 100644 --- a/reference/add_summary.html +++ b/reference/add_summary.html @@ -1,5 +1,5 @@ -Generate a summary from a data frame of prompts — add_summary • biorecap +Generate a summary from a data frame of prompts — add_summary • biorecap Skip to contents @@ -26,8 +26,7 @@
- -

Generate a summary from a data frame of prompts

+

Generate a summary from a data frame of prompts

Source: R/biorecap.R
add_summary.Rd
diff --git a/reference/biorecap-package.html b/reference/biorecap-package.html index 7d5aab1..12d512c 100644 --- a/reference/biorecap-package.html +++ b/reference/biorecap-package.html @@ -1,5 +1,7 @@ -biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama — biorecap-package • biorecap +biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama — biorecap-package • biorecap Skip to contents @@ -26,14 +28,14 @@
- -

biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama

+

biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama

Source: R/biorecap-package.R
biorecap-package.Rd
-

Retrieve and summarize bioRxiv preprints with a local LLM using ollama.

+

+

Retrieve and summarize bioRxiv preprints with a local LLM using ollama.

diff --git a/reference/biorecap_report.html b/reference/biorecap_report.html index af3ea05..07bbd22 100644 --- a/reference/biorecap_report.html +++ b/reference/biorecap_report.html @@ -1,5 +1,5 @@ -Create a report from bioRxiv preprints — biorecap_report • biorecap +Create a report from bioRxiv preprints — biorecap_report • biorecap Skip to contents @@ -26,8 +26,7 @@
- -

Create a report from bioRxiv preprints

+

Create a report from bioRxiv preprints

Source: R/biorecap.R
biorecap_report.Rd
diff --git a/reference/build_prompt_preprint.html b/reference/build_prompt_preprint.html index 8a6f131..c174f1e 100644 --- a/reference/build_prompt_preprint.html +++ b/reference/build_prompt_preprint.html @@ -1,5 +1,5 @@ -Construct a prompt to summarize a paper — build_prompt_preprint • biorecap +Construct a prompt to summarize a paper — build_prompt_preprint • biorecap Skip to contents @@ -26,8 +26,7 @@
- -

Construct a prompt to summarize a paper

+

Construct a prompt to summarize a paper

Source: R/biorecap.R
build_prompt_preprint.Rd
diff --git a/reference/build_prompt_subject.html b/reference/build_prompt_subject.html index 60b6361..5b06f16 100644 --- a/reference/build_prompt_subject.html +++ b/reference/build_prompt_subject.html @@ -1,5 +1,5 @@ -Construct a prompt to summarize a set of papers from a subject — build_prompt_subject • biorecap +Construct a prompt to summarize a set of papers from a subject — build_prompt_subject • biorecap Skip to contents @@ -26,8 +26,7 @@
- -

Construct a prompt to summarize a set of papers from a subject

+

Construct a prompt to summarize a set of papers from a subject

Source: R/biorecap.R
build_prompt_subject.Rd
diff --git a/reference/example_preprints.html b/reference/example_preprints.html index 5ee7953..c53468a 100644 --- a/reference/example_preprints.html +++ b/reference/example_preprints.html @@ -1,5 +1,5 @@ -Example preprints with summaries — example_preprints • biorecap +Example preprints with summaries — example_preprints • biorecap Skip to contents @@ -26,8 +26,7 @@
- -

Example preprints with summaries

+

Example preprints with summaries

Source: R/data.R
example_preprints.Rd
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zzveJ(-B&wcjy|V;(~0YM;8OU+&a5bTMEfLj`}&^eo^Ma}vybpm6j<0Y>%ZiDF?w6E zMtH9fkeOtfdZVt9&r1vcOGI$k=m2g}ZwG8W%2Ti+SQWsFv2hho);mz($kY)h#2iPl z!2xm&-4Ch!_SHVzta>VSW;Avo!h_uwSlCEPy+Wt{0AuM3%%5b|uHuA;k}#GL=IZz& zVI~^lc!_(+|6pSQ^|MZru6;M&LF>S}Lm~k-AJt%wA>8-~39H*dp$L{V{Y#D^&BqHE zAkqA*vXQ!;7qk@AIOKlneN?7YZ@%$+` z^)D3Q3A)_bau%rMraaT1p~lk`NYuU)RuiZ(9f0)3@z=VB&H!dHF4n9*Faq3&l!&b? zqt(%uG*I+j-&O9?HVx*uS!VPEtJ+oaY{ZwFS^x>%g-{=5@g=!}GLjH7dDQO{NUoM$mh@$|#^) -Get bioRxiv preprints — get_preprints • biorecap +Get bioRxiv preprints — get_preprints • biorecap Skip to contents @@ -26,8 +26,7 @@
- -

Get bioRxiv preprints

+

Get bioRxiv preprints

Source: R/biorecap.R
get_preprints.Rd
@@ -73,16 +72,16 @@

Examples#> # A tibble: 60 × 4 #> subject title url abstract #> <chr> <chr> <chr> <chr> -#> 1 bioinformatics Tesorai Search: Large pretrained model boosts … http… Current… -#> 2 bioinformatics DIAMOND2GO: A rapid Gene Ontology assignment a… http… DIAMOND… -#> 3 bioinformatics Multi-Modal Large Language Model Enables Prote… http… Predict… -#> 4 bioinformatics Sawfish: Improving long-read structural varian… http… Motivat… -#> 5 bioinformatics Towards Digital Quantification of Ploidy from … http… Abnorma… -#> 6 bioinformatics Interdependent regulation of alternative splic… http… Alterna… -#> 7 bioinformatics UnigeneFinder: An automated pipeline for gene … http… For mos… -#> 8 bioinformatics Normalization of Single-cell RNA-seq Data Usin… http… Normali… -#> 9 bioinformatics Phytochemical Analysis and Cytotoxic Effects o… http… Hepatoc… -#> 10 bioinformatics mobileRNA: a tool for efficient analysis of mo… http… In plan… +#> 1 bioinformatics Unsupervised domain classification of AlphaFol… http… The rel… +#> 2 bioinformatics Testing and overcoming the limitations of Modu… http… Modular… +#> 3 bioinformatics Decoding multicellular niche formation in the … http… Accurat… +#> 4 bioinformatics Insights from Molecular Docking and Dynamics S… http… Alpha-s… +#> 5 bioinformatics TUSCAN: Tumor segmentation and classification … http… The ide… +#> 6 bioinformatics Characterizing the role of exosomal miRNAs in … http… Backgro… +#> 7 bioinformatics The Lomb-Scargle periodogram-based differentia… http… Motivat… +#> 8 bioinformatics Protein stability models fail to capture epist… http… There i… +#> 9 bioinformatics CryptoBench: Cryptic protein-ligand binding si… http… Structu… +#> 10 bioinformatics haCCA: Multi-module Integrating of spatial tra… http… Spatial… #> # ℹ 50 more rows

diff --git a/reference/index.html b/reference/index.html index b8b74cb..09217f9 100644 --- a/reference/index.html +++ b/reference/index.html @@ -1,5 +1,5 @@ -Package index • biorecap +Package index • biorecap Skip to contents @@ -26,8 +26,7 @@
- -

Package index

+

Package index

diff --git a/reference/reexports.html b/reference/reexports.html index b0290df..d82a4e1 100644 --- a/reference/reexports.html +++ b/reference/reexports.html @@ -1,5 +1,5 @@ -Objects exported from other packages — reexports • biorecapObjects exported from other packages — reexports • biorecap +"> Skip to contents @@ -40,8 +40,7 @@
- -

Objects exported from other packages

+

Objects exported from other packages

Source: R/reexports.R
reexports.Rd
diff --git a/reference/subjects.html b/reference/subjects.html index e66a7ba..10f2560 100644 --- a/reference/subjects.html +++ b/reference/subjects.html @@ -1,5 +1,5 @@ -bioRxiv subjects — subjects • biorecap +bioRxiv subjects — subjects • biorecap Skip to contents @@ -26,8 +26,7 @@
- -

bioRxiv subjects

+

bioRxiv subjects

Source: R/data.R
subjects.Rd
diff --git a/reference/tt_preprints.html b/reference/tt_preprints.html index 0261958..03618f1 100644 --- a/reference/tt_preprints.html +++ b/reference/tt_preprints.html @@ -1,5 +1,5 @@ -Create a markdown table from prepreprint summaries — tt_preprints • biorecap +Create a markdown table from prepreprint summaries — tt_preprints • biorecap Skip to contents @@ -26,8 +26,7 @@
- -

Create a markdown table from prepreprint summaries

+

Create a markdown table from prepreprint summaries

Source: R/biorecap.R
tt_preprints.Rd
diff --git a/search.json b/search.json index 7b8824f..30bacd3 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to biorecap","title":"Contributing to biorecap","text":"outlines propose change biorecap. detailed discussion contributing tidyverse packages, please see development contributing guide code review principles.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to biorecap","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to biorecap","text":"want make bigger change, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed). See guide create great issue advice.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to biorecap","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"stephenturner/biorecap\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Bigger changes","what":"Code style","title":"Contributing to biorecap","text":"New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. use roxygen2, Markdown syntax, documentation. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to biorecap","text":"Please note biorecap project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://stephenturner.github.io/biorecap/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2024 Stephen Turner Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://stephenturner.github.io/biorecap/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Stephen Turner. Author, maintainer.","code":""},{"path":"https://stephenturner.github.io/biorecap/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Turner S (2024). biorecap: Retrieve summarize bioRxiv preprints local LLM using ollama. R package version 0.1.0, https://stephenturner.github.io/biorecap/.","code":"@Manual{, title = {biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama}, author = {Stephen Turner}, year = {2024}, note = {R package version 0.1.0}, url = {https://stephenturner.github.io/biorecap/}, }"},{"path":"https://stephenturner.github.io/biorecap/index.html","id":"biorecap","dir":"","previous_headings":"","what":"Retrieve and summarize bioRxiv preprints with a local LLM using ollama","title":"Retrieve and summarize bioRxiv preprints with a local LLM using ollama","text":"Retrieve summarize bioRxiv preprints using local LLM Ollama via ollamar.","code":""},{"path":"https://stephenturner.github.io/biorecap/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Retrieve and summarize bioRxiv preprints with a local LLM using ollama","text":"Install biorecap GitHub (keep dependencies=TRUE get Suggests packages needed create HTML report):","code":"# install.packages(\"remotes\") remotes::install_github(\"stephenturner/biorecap\", dependencies=TRUE)"},{"path":[]},{"path":"https://stephenturner.github.io/biorecap/index.html","id":"quick-start","dir":"","previous_headings":"Usage","what":"Quick start","title":"Retrieve and summarize bioRxiv preprints with a local LLM using ollama","text":"First, load biorecap library. Let’s make sure Ollama running can talk R: Next can list available models: Write HTML report containing summaries recent preprints select subject areas current working directory. Example HTML report generated bioRxiv RSS feed August 6, 2024:","code":"library(biorecap) test_connection() #> Ollama local server running #> #> GET http://localhost:11434/ #> Status: 200 OK #> Content-Type: text/plain #> Body: In memory (17 bytes) list_models() #> # A tibble: 3 × 4 #> name model parameter_size quantization_level #> #> 1 gemma2:latest gemma2:latest 9.2B Q4_0 #> 2 llama3.1:latest llama3.1:latest 8.0B Q4_0 #> 3 llama3.1:70b llama3.1:70b 70.6B Q4_0 biorecap_report(output_dir=\".\", subject=c(\"bioinformatics\", \"genomics\", \"synthetic_biology\"), model=\"llama3.1\")"},{"path":"https://stephenturner.github.io/biorecap/index.html","id":"details","dir":"","previous_headings":"Usage","what":"Details","title":"Retrieve and summarize bioRxiv preprints with a local LLM using ollama","text":"get_preprints() function retrieves preprints bioRxiv’s RSS feeds. pass one subjects subject argument. add_prompt() function adds prompt preprint used prompt model. Let’s take look one prompts: giving paper’s title abstract. Summarize paper many sentences instruct. include preamble text. Just give summary. Number sentences summary: 2 Title: SeuratExtend: Streamlining Single-Cell RNA-Seq Analysis Integrated Intuitive Framework Abstract: Single-cell RNA sequencing (scRNA-seq) revolutionized study cellular heterogeneity, rapid expansion analytical tools proven blessing curse, presenting researchers significant challenges. , present SeuratExtend, comprehensive R package built upon widely adopted Seurat framework, streamlines scRNA-seq data analysis integrating essential tools databases. SeuratExtend offers user-friendly intuitive interface performing wide range analyses, including functional enrichment, trajectory inference, gene regulatory network reconstruction, denoising. package seamlessly integrates multiple databases, Gene Ontology Reactome, incorporates popular Python tools like scVelo, Palantir, SCENIC unified R interface. SeuratExtend enhances data visualization optimized plotting functions carefully curated color schemes, ensuring aesthetic appeal scientific rigor. demonstrate SeuratExtend’s performance case studies investigating tumor-associated high-endothelial venules autoinflammatory diseases, showcase novel applications pathway-Level analysis cluster annotation. SeuratExtend empowers researchers harness full potential scRNA-seq data, making complex analyses accessible wider audience. package, along comprehensive documentation tutorials, freely available GitHub, providing valuable resource single-cell genomics community. add_summary() function uses locally running LLM available Ollama summarize preprint. Let’s add summary. Notice can single pipeline. takes minutes! Let’s take look results: Let’s look one summaries. ’s summary SeuratExtend paper (abstract ): SeuratExtend R package integrates essential tools databases single-cell RNA sequencing (scRNA-seq) data analysis, streamlining process user-friendly interface. package offers various analyses, including functional enrichment gene regulatory network reconstruction, seamlessly integrates multiple databases popular Python tools. biorecap_report() function runs code RMarkdown template, writing resulting HTML CSV file results current working directory. built-subjects vector contains available bioRxiv subjects. create report subjects like (note, take time):","code":"pp <- get_preprints(subject=c(\"bioinformatics\", \"genomics\", \"synthetic_biology\")) pp #> # A tibble: 90 × 4 #> subject title url abstract #> #> 1 bioinformatics Integrity and miss grouping as support for clu… http… \"The hi… #> 2 bioinformatics Sainsc: a computational tool for segmentation-… http… \"Spatia… #> 3 bioinformatics BRACE: A novel Bayesian-based imputation appro… http… \"Bayesi… #> 4 bioinformatics Topological embedding and directional feature … http… \"Cancer… #> 5 bioinformatics SeuratExtend: Streamlining Single-Cell RNA-Seq… http… \"Single… #> 6 bioinformatics An Evolutionary Statistics Toolkit for Simplif… http… \"We pre… #> 7 bioinformatics A map of integrated cis-regulatory elements en… http… \"Cis-re… #> 8 bioinformatics MOSTPLAS: A Self-correction Multi-label Learni… http… \"Plasmi… #> 9 bioinformatics Bootstrap Evaluation of Association Matrices (… http… \"Motiva… #> 10 bioinformatics Thermodynamic modeling of Csr/Rsm- RNA interac… http… \"Backgr… #> # ℹ 80 more rows pp <- pp |> add_prompt() pp #> # A tibble: 90 × 5 #> subject title url abstract prompt #> #> 1 bioinformatics Integrity and miss grouping as support … http… \"The hi… \"I am… #> 2 bioinformatics Sainsc: a computational tool for segmen… http… \"Spatia… \"I am… #> 3 bioinformatics BRACE: A novel Bayesian-based imputatio… http… \"Bayesi… \"I am… #> 4 bioinformatics Topological embedding and directional f… http… \"Cancer… \"I am… #> 5 bioinformatics SeuratExtend: Streamlining Single-Cell … http… \"Single… \"I am… #> 6 bioinformatics An Evolutionary Statistics Toolkit for … http… \"We pre… \"I am… #> 7 bioinformatics A map of integrated cis-regulatory elem… http… \"Cis-re… \"I am… #> 8 bioinformatics MOSTPLAS: A Self-correction Multi-label… http… \"Plasmi… \"I am… #> 9 bioinformatics Bootstrap Evaluation of Association Mat… http… \"Motiva… \"I am… #> 10 bioinformatics Thermodynamic modeling of Csr/Rsm- RNA … http… \"Backgr… \"I am… #> # ℹ 80 more rows pp <- get_preprints(subject=c(\"bioinformatics\", \"genomics\", \"synthetic_biology\")) |> add_prompt() |> add_summary(model=\"llama3.1\") pp #> # A tibble: 90 × 6 #> subject title url abstract prompt summary #> #> 1 bioinformatics Integrity and miss grouping as … http… \"The hi… \"I am… \"The p… #> 2 bioinformatics Sainsc: a computational tool fo… http… \"Spatia… \"I am… \"Sains… #> 3 bioinformatics BRACE: A novel Bayesian-based i… http… \"Bayesi… \"I am… \"Alter… #> 4 bioinformatics Topological embedding and direc… http… \"Cancer… \"I am… \"Resea… #> 5 bioinformatics SeuratExtend: Streamlining Sing… http… \"Single… \"I am… \"Seura… #> 6 bioinformatics An Evolutionary Statistics Tool… http… \"We pre… \"I am… \"The \\… #> 7 bioinformatics A map of integrated cis-regulat… http… \"Cis-re… \"I am… \"The a… #> 8 bioinformatics MOSTPLAS: A Self-correction Mul… http… \"Plasmi… \"I am… \"Plasm… #> 9 bioinformatics Bootstrap Evaluation of Associa… http… \"Motiva… \"I am… \"The a… #> 10 bioinformatics Thermodynamic modeling of Csr/R… http… \"Backgr… \"I am… \"Resea… #> # ℹ 80 more rows biorecap_report(output_dir=\".\", subject=c(\"bioinformatics\", \"genomics\", \"synthetic_biology\"), model=\"llama3.1\") subjects #> [1] \"all\" #> [2] \"animal_behavior_and_cognition\" #> [3] \"biochemistry\" #> [4] \"bioengineering\" #> [5] \"bioinformatics\" #> [6] \"biophysics\" #> [7] \"cancer_biology\" #> [8] \"cell_biology\" #> [9] \"clinical_trials\" #> [10] \"developmental_biology\" #> [11] \"ecology\" #> [12] \"epidemiology\" #> [13] \"evolutionary_biology\" #> [14] \"genetics\" #> [15] \"genomics\" #> [16] \"immunology\" #> [17] \"microbiology\" #> [18] \"molecular_biology\" #> [19] \"neuroscience\" #> [20] \"paleontology\" #> [21] \"pathology\" #> [22] \"pharmacology_and_toxicology\" #> [23] \"plant_biology\" #> [24] \"scientific_communication_and_education\" #> [25] \"synthetic_biology\" #> [26] \"systems_biology\" #> [27] \"zoology\" biorecap_report(output_dir=\".\", subject=subjects, model=\"llama3.1\")"},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":null,"dir":"Reference","previous_headings":"","what":"Add prompt to a data frame of preprints — add_prompt","title":"Add prompt to a data frame of preprints — add_prompt","text":"Add prompt data frame preprints","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add prompt to a data frame of preprints — add_prompt","text":"","code":"add_prompt(preprints, ...)"},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add prompt to a data frame of preprints — add_prompt","text":"preprints Result get_preprints(). ... Additional arguments build_prompt_preprint().","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add prompt to a data frame of preprints — add_prompt","text":"data frame bioRxiv preprints prompt added.","code":""},{"path":[]},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add prompt to a data frame of preprints — add_prompt","text":"","code":"preprints <- get_preprints(subject=c(\"bioinformatics\", \"genomics\")) preprints <- add_prompt(preprints) preprints #> # A tibble: 60 × 5 #> subject title url abstract prompt #> #> 1 bioinformatics Tesorai Search: Large pretrained model … http… Current… \"I am… #> 2 bioinformatics DIAMOND2GO: A rapid Gene Ontology assig… http… DIAMOND… \"I am… #> 3 bioinformatics Multi-Modal Large Language Model Enable… http… Predict… \"I am… #> 4 bioinformatics Sawfish: Improving long-read structural… http… Motivat… \"I am… #> 5 bioinformatics Towards Digital Quantification of Ploid… http… Abnorma… \"I am… #> 6 bioinformatics Interdependent regulation of alternativ… http… Alterna… \"I am… #> 7 bioinformatics UnigeneFinder: An automated pipeline fo… http… For mos… \"I am… #> 8 bioinformatics Normalization of Single-cell RNA-seq Da… http… Normali… \"I am… #> 9 bioinformatics Phytochemical Analysis and Cytotoxic Ef… http… Hepatoc… \"I am… #> 10 bioinformatics mobileRNA: a tool for efficient analysi… http… In plan… \"I am… #> # ℹ 50 more rows"},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":null,"dir":"Reference","previous_headings":"","what":"Add prompts for an entire subject — add_prompt_subject","title":"Add prompts for an entire subject — add_prompt_subject","text":"Add prompts entire subject","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add prompts for an entire subject — add_prompt_subject","text":"","code":"add_prompt_subject(preprints, ...)"},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add prompts for an entire subject — add_prompt_subject","text":"preprints Output get_preprints() followed add_prompt() followed add_summary(). ... Additional arguments build_prompt_subject().","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add prompts for an entire subject — add_prompt_subject","text":"tibble subject prompt column.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add prompts for an entire subject — add_prompt_subject","text":"","code":"subjects <- example_preprints |> dplyr::group_by(subject) |> add_prompt_subject() #> Warning: Expecting a tibble of class 'preprints_prompt' returned from get_preprints() |> add_prompt(). subjects #> # A tibble: 3 × 2 #> subject prompt #> #> 1 bioinformatics \"I am giving you information about preprints published in b… #> 2 genomics \"I am giving you information about preprints published in b… #> 3 synthetic_biology \"I am giving you information about preprints published in b…"},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a summary from a data frame of prompts — add_summary","title":"Generate a summary from a data frame of prompts — add_summary","text":"Generate summary data frame prompts","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a summary from a data frame of prompts — add_summary","text":"","code":"add_summary(preprints, model = \"llama3.1\")"},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a summary from a data frame of prompts — add_summary","text":"preprints Output get_preprints() followed add_prompt(). model model available Ollama (run ollamar::list_models()) see available.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a summary from a data frame of prompts — add_summary","text":"tibble, response column added.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a summary from a data frame of prompts — add_summary","text":"","code":"if (FALSE) { # \\dontrun{ # Individual papers preprints <- get_preprints(c(\"genomics\", \"bioinformatics\")) |> add_prompt() |> add_summary() preprints } # }"},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap-package.html","id":null,"dir":"Reference","previous_headings":"","what":"biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama — biorecap-package","title":"biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama — biorecap-package","text":"Retrieve summarize bioRxiv preprints local LLM using ollama.","code":""},{"path":[]},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama — biorecap-package","text":"Maintainer: Stephen Turner vustephen@gmail.com (ORCID)","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a report from bioRxiv preprints — biorecap_report","title":"Create a report from bioRxiv preprints — biorecap_report","text":"Create report bioRxiv preprints","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a report from bioRxiv preprints — biorecap_report","text":"","code":"biorecap_report( output_dir = \".\", subject = NULL, nsentences = 2L, model = \"llama3.1\", use_example_preprints = FALSE, ... )"},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a report from bioRxiv preprints — biorecap_report","text":"output_dir Directory save report. subject Character vector subjects include report. nsentences Number sentences summarize paper . model model use generating summaries. See ollamar::list_models(). use_example_preprints Use example preprints data included package instead fetching new data bioRxiv. diagnostic/testing purposes . ... arguments passed rmarkdown::render().","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a report from bioRxiv preprints — biorecap_report","text":"Nothing; called side effects produce report.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a report from bioRxiv preprints — biorecap_report","text":"","code":"if (FALSE) { # \\dontrun{ output_dir <- tempdir() biorecap_report(use_example_preprints=TRUE, output_dir=output_dir) biorecap_report(subject=c(\"bioinformatics\", \"genomics\", \"synthetic_biology\"), output_dir=output_dir) } # }"},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct a prompt to summarize a paper — build_prompt_preprint","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"Construct prompt summarize paper","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"","code":"build_prompt_preprint( title, abstract, nsentences = 2L, instructions = c(\"I am giving you a paper's title and abstract.\", \"Summarize the paper in as many sentences as I instruct.\", \"Do not include any preamble text to the summary\", \"just give me the summary with no preface or intro sentence.\") )"},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"title title paper. abstract abstract paper. nsentences number sentences summarize paper . instructions Instructions prompt. can character vector gets collapsed single string.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"string containing prompt.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"","code":"build_prompt_preprint(title=\"A great paper\", abstract=\"This is the abstract.\") #> [1] \"I am giving you a paper's title and abstract. Summarize the paper in as many sentences as I instruct. Do not include any preamble text to the summary just give me the summary with no preface or intro sentence.\\nNumber of sentences in summary: 2\\nTitle: A great paper\\nAbstract: This is the abstract.\""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"Construct prompt summarize set papers subject","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"","code":"build_prompt_subject( subject, title, summary, nsentences = 5L, instructions = c(\"I am giving you information about preprints published in bioRxiv recently.\", \"I'll give you the subject, preprint titles, and short summary of each paper.\", \"Please provide a general summary new advances in this subject/field in general.\", \"Provide this summary of the field in as many sentences as I instruct.\", \"Do not include any preamble text to the summary\", \"just give me the summary with no preface or intro sentence.\") )"},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"subject name subject. title character vector titles subject summary character vector summaries paper provided get_preprints() followed add_prompt() followed add_summary(). nsentences number sentences summarize subject . instructions Instructions prompt. can character vector gets collapsed single string.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"string containing prompt.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"","code":"title <- example_preprints |> dplyr::filter(subject==\"bioinformatics\") |> dplyr::pull(title) summary <- example_preprints |> dplyr::filter(subject==\"bioinformatics\") |> dplyr::pull(summary) build_prompt_subject(subject=\"bioinformatics\", title=title, summary=summary) #> [1] \"I am giving you information about preprints published in bioRxiv recently. I'll give you the subject, preprint titles, and short summary of each paper. Please provide a general summary new advances in this subject/field in general. Provide this summary of the field in as many sentences as I instruct. Do not include any preamble text to the summary just give me the summary with no preface or intro sentence.\\n\\nSubject: bioinformatics\\nNumber of sentences in summary: 5\\n\\nHere are the titles and summaries:\\n\\nTitle: Integrity and miss grouping as support for clusters in agglomerative hierarchical methods: the R-package octopucs\\nSummary: The proposed method assesses cluster support throughout hierarchical analyses by compiling a consensus topology and using ecological concepts of reciprocal complementarities to define cluster integrity and contamination. This approach allows for building support for groups even when there is partial membership match after resampling, and was implemented in the R package octopucs, which showed robust detection of changes in group memberships compared to other methods.\\n\\nTitle: Sainsc: a computational tool for segmentation-free analysis of in-situ capture\\nSummary: Sainsc is a computational tool that enables segmentation-free analysis of spatially resolved transcriptomics data, allowing for accurate cell-type assignment at the subcellular level without requiring manual cell border delineation. The tool provides efficient processing of high-resolution spatial data and can generate maps of cell types with corresponding confidence scores, making it a valuable resource for biomedical researchers working with complex tissue samples.\\n\\nTitle: BRACE: A novel Bayesian-based imputation approach for dimension reduction analysis of alternative splicing at single-cell resolution\\nSummary: Alternative splicing represents an additional layer of complexity in gene expression profiles, but analyzing it at single-cell resolution is challenging due to missing data. This paper introduces BRACE, a Bayesian-based imputation approach that improves upon existing methods and enables dimension reduction analysis of alternative splicing events at single-cell resolution.\\n\\nTitle: Topological embedding and directional feature importance in ensemble classifiers for multi-class classification\\nSummary: Researchers developed a new metric called class-based direction feature importance (CLIFI) to provide interpretable insights into the decision-making process of ensemble classifiers for multi-class classification problems, specifically in the context of cancer biomarker identification. The CLIFI metric was incorporated into four algorithms and applied to The Cancer Genome Atlas proteomics data, resulting in high F1-scores and allowing for the visualization of model decision-making functions and the identification of heterogeneity in several proteins across different cancer types.\\n\\nTitle: SeuratExtend: Streamlining Single-Cell RNA-Seq Analysis Through an Integrated and Intuitive Framework\\nSummary: SeuratExtend is an R package that integrates essential tools and databases for single-cell RNA sequencing (scRNA-seq) data analysis, streamlining the process through a user-friendly interface. The package offers various analyses, including functional enrichment and gene regulatory network reconstruction, and seamlessly integrates multiple databases and popular Python tools.\\n\\nTitle: An Evolutionary Statistics Toolkit for Simplified Sequence Analysis on Web with Client-Side Processing\\nSummary: The \\\"Evolutionary Statistics Toolkit\\\" is a web-based platform that integrates multiple evolutionary statistics tools for simplified sequence analysis, including Tajima's D calculator and Shannon's Entropy. The open-source toolkit facilitates streamlined workflows for researchers in evolutionary biology and genomics, and also serves as an educational interactive website for beginners in evolutionary statistics.\\n\\nTitle: A map of integrated cis-regulatory elements enhances gene regulatory analysis in maize\\nSummary: The authors integrated various methods for profiling cis-regulatory elements (CREs) in maize, resulting in maps of integrated CREs that show increased completeness and precision. These maps were used to infer drought-specific gene regulatory networks and identify candidate regulators of maize drought response, as well as to study the potential role of transposable elements in regulating gene expression.\\n\\nTitle: MOSTPLAS: A Self-correction Multi-label Learning Model for Plasmid Host Range Prediction\\nSummary: Plasmid host range prediction tools are essential for understanding how plasmids promote bacterial evolution, but existing learning-based tools struggle due to limited well-annotated training samples. The proposed model, MOSTPLAS, addresses this issue with a self-correction multi-label learning approach that uses pseudo label learning and asymmetric loss to facilitate training with incomplete labels.\\n\\nTitle: Bootstrap Evaluation of Association Matrices (BEAM) for Integrating Multiple Omics Profiles with Multiple Outcomes\\nSummary: The authors propose Bootstrap Evaluation of Association Matrices (BEAM), a new statistical method that integrates multiple omics profiles with multiple clinical endpoints to identify significant associations between them. BEAM outperformed other integrated analysis methods in simulations and identified biologically relevant genes in a pediatric leukemia application that were missed by univariate screens and other methods.\\n\\nTitle: Thermodynamic modeling of Csr/Rsm- RNA interactions capture novel, direct binding interactions across the Pseudomonas aeruginosa transcriptome\\nSummary: Researchers developed a thermodynamic model to predict interactions between the post-transcriptional regulator RsmA and mRNAs in Pseudomonas aeruginosa, predicting 1043 direct binding interactions, including 457 novel targets. The predictions were validated through in vitro binding assays and in vivo translational reporters, revealing direct regulation of genes involved in quorum sensing and the Type IV Secretion system, expanding the known pool of RsmA target genes.\\n\\nTitle: Assessing the ability of ChatGPT to extract natural product bioactivity and biosynthesis data from publications\\nSummary: ChatGPT was tested on its ability to extract data from publications on natural product bioactivity and biosynthesis, which is crucial for training models that predict natural product activity from biosynthetic gene clusters. The results showed that ChatGPT performed well in identifying papers describing natural product discovery and extracting information about the product's bioactivity, but struggled with extracting accession numbers for the biosynthetic gene cluster or producer's genome.\\n\\nTitle: Genome-Wide Analysis of TCP Family Genes and Their Constitutive Expression Pattern Analysis in the Melon (Cucumis melo)\\nSummary: This study identified and characterized 29 putative TCP genes in melon, classifying them into two classes and analyzing their chromosomal location, gene structure, and expression patterns. The results suggest that some CmTCP genes may have similar functions to their homologs in other plant species, while others may have undergone functional diversification, providing a resource for future investigations into their roles in melon development.\\n\\nTitle: Single-cell differential expression analysis between conditions within nested settings\\nSummary: Researchers compared various methods for differential expression analysis of single-cell transcriptomics data and found that methods designed specifically for single-cell data do not offer performance advantages over conventional pseudobulk methods like DESeq2 when applied to individual datasets. However, permutation-based methods excel in performance for atlas-level analysis, but require significantly longer run times, making DREAM a compromise between quality and runtime.\\n\\nTitle: CoMPHI: A Novel Composite Machine Learning Approach Utilizing Multiple FeatureRepresentation to Predict Hosts of Bacteriophages\\nSummary: Here is a 2-sentence summary of the paper: This study introduces CoMPHI, a novel composite machine learning approach that combines multiple feature representations to predict hosts of bacteriophages, with potential applications in phage therapy for treating bacterial infections. The model achieves high prediction accuracy, with an Area Under the ROC Curve (AUC) of up to 96.7% and accuracy of up to 95.1%, outperforming existing methods due to its inclusion of alignment scores and use of both nucleotide and protein sequences from phages and hosts.\\n\\nTitle: FourierMIL: Fourier filtering-based multiple instance learning for whole slide image analysis\\nSummary: The paper presents FourierMIL, a multiple instance learning framework that uses the discrete Fourier transform to analyze whole-slide images (WSIs) in digital pathology. The method captures both global and local dependencies within WSIs and outperforms existing state-of-the-art methods in tumor classification tasks on gigapixel-resolution WSIs.\\n\\nTitle: Multiple Protein Structure Alignment at Scale with FoldMason\\nSummary: Here is a 2-sentence summary of the paper: FoldMason is a new method for multiple protein structure alignment that can handle hundreds of thousands of structures at scale with high speed and accuracy. It leverages the structural alphabet from Foldseek to compute confidence scores, provide interactive visualizations, and support large-scale protein structure analysis and phylogenetic studies.\\n\\nTitle: Deciphering octoploid strawberry evolution with serial LTR similarity matrices for subgenome partition\\nSummary: A novel approach was developed to assign polyploid genome assemblies to subgenomes using long terminal repeat retrotransposons (LTR-RTs) and the Serial Similarity Matrix (SSM) method, which is particularly useful for genomes without known diploid ancestors. The SSM approach was validated using well-studied allopolyploidy genomes and then applied to the octoploid strawberry genome, revealing three allopolyploidization events in its evolutionary history.\\n\\nTitle: IDENTIFICATION OF IMMUNE RESPONSE AND RNA NETWORK OF RHEUMATOID ARTHRITIS AND MOLECULAR DOCKING OF CELASTRUS PANICULATUS AS POTENTIAL THERAPEUTIC AGENT\\nSummary: This study used bioinformatics analysis to identify immune responses, microRNA-hub genes networks, and potential therapeutic agents for rheumatoid arthritis (RA), a complex autoimmune disease with an unknown pathogenesis. The researchers found several hub genes and miRNAs associated with RA, and identified oleic acid and zeylasterone as potential novel drug candidates against the disease through molecular docking analysis of Celastrus paniculatus phytochemical compounds.\\n\\nTitle: Imputing abundance of over 2500 surface proteins from single-cell transcriptomes with context-agnostic zero-shot deep ensembles\\nSummary: SPIDER is a deep ensemble model that predicts the abundance of over 2500 surface proteins from single-cell transcriptomes with improved generalization across diverse contexts such as tissues or disease states. The model outperforms other state-of-the-art methods and has various applications including cell type annotation, biomarker/target identification, and cell-cell interaction analysis in cancer research.\\n\\nTitle: Modelling Protein-Glycan Interactions with HADDOCK\\nSummary: Glycans play important roles in living organisms by interacting with proteins for information transfer and signalling purposes, making it essential to determine the three-dimensional structures of protein-glycan complexes. The molecular docking approach HADDOCK was used to predict protein-glycan complexes with a top 5 success rate of 70% for bound datasets and 40% for unbound datasets using a benchmark of 89 complexes.\\n\\nTitle: Machine Learning Reveals Key Glycoprotein Mutations and Rapidly Assigns Lassa Virus Lineages\\nSummary: Machine learning and phylogenetic analysis of Lassa virus glycoprotein sequences revealed key mutations and genetic differences between Nigerian lineages and those from other West African countries. The study identified specific amino acid positions that are highly variable among the lineages, which may explain structural and phenotypical differences, and developed a machine learning-based tool for rapid lineage classification.\\n\\nTitle: RESP2: An uncertainty aware multi-target multi-property optimization AI pipeline for antibody discovery\\nSummary: The RESP2 pipeline is an AI-powered tool designed to optimize the discovery of therapeutic antibodies against infectious disease pathogens, taking into account multiple targets and properties such as specificity, low immunogenicity, and high affinity. The pipeline uses a suite of methods to estimate uncertainty in predictions and has been successfully applied to discover a highly human antibody with broad binding to variants of the COVID-19 spike protein receptor binding domain.\\n\\nTitle: Extending the capabilities of deconvolution to provide cell type specific pathway analysis of bulk RNA-seq data for idiopathic pulmonary fibrosis\\nSummary: A deconvolution method was applied to bulk RNA-seq data from idiopathic pulmonary fibrosis (IPF) samples to correct for changes in cell type proportions and provide cell-type specific pathway analysis. The results showed significant increases in fibroblasts and myofibroblasts, decreases in vascular endothelial capillary cells, and IPF-related changes in extracellular matrix organization and TGF-{beta} regulation, as well as the involvement of interferon signaling in ATII cells.\\n\\nTitle: A survey of ADP-ribosyltransferase families in the pathogenic Legionella\\nSummary: A comprehensive bioinformatic survey of 41 Legionella species identified 63 proteins with significant sequence or structural similarity to known ADP-ribosyltransferases (ARTs), organized into 39 ART-like families, including 26 novel families. The study found that most members of the novel ART families are predicted effectors, presenting promising targets for understanding Legionella pathogenicity and developing therapeutic strategies.\\n\\nTitle: A replicable and modular benchmark for long-read transcript quantification methods\\nSummary: Researchers have developed a replicable benchmark for evaluating long-read transcript quantification methods using synthetic RNA-seq datasets, which can be easily extended to include new tools or data sets. The study reveals discrepancies with previously published results and highlights the importance of high-quality simulated data in assessing the robustness of certain approaches.\\n\\nTitle: Logan: Planetary-Scale Genome Assembly Surveys Life's Diversity\\nSummary: The NCBI Sequence Read Archive contains over 50 petabases of DNA sequencing data across 27 million datasets, but its size makes it impractical to search for specific genetic sequences within a reasonable time frame. To address this issue, the authors used cloud computing to perform genome assembly on each dataset and created the Logan assemblage, which is now freely available and enables faster querying of the data, with some queries completing in as little as 11 hours.\\n\\nTitle: Cell-type specific epigenetic clocks to quantify biological age at cell-type resolution\\nSummary: Epigenetic clocks have been developed to estimate biological age, but most are based on heterogeneous bulk tissues and reflect both changes in cell-type composition and individual cell aging. This study created neuron- and hepatocyte-specific DNA methylation clocks that provide improved estimates of chronological age and detect accelerated biological aging in Alzheimer's disease and liver pathology.\\n\\nTitle: Genomic and transcriptomic analyses of Heteropoda venatoria reveal the expansion of P450 family for starvation resistance in spider\\nSummary: The genome of Heteropoda venatoria was sequenced and comparative genomic analysis revealed significant expansions in gene families related to lipid metabolism, including cytochrome P450 and steroid hormone biosynthesis genes. The study found that during starvation, H. venatoria undergoes a series of physiological changes, including the activation of fatty acid metabolism and protein degradation pathways, and the expression of expanded P450 gene families, which help the spider maintain a low-energy metabolic state and endure longer periods of starvation.\\n\\nTitle: Annotation Vocabulary (Might Be) All You Need\\nSummary: The authors introduce the \\\"Annotation Vocabulary\\\", a language of protein properties defined by structured ontologies that can be used to train transformer models without reference to amino acid sequences. They demonstrate the effectiveness of this approach in various experiments, achieving state-of-the-art results on several common datasets with competitive performance on others, and generating high-quality de novo protein sequences from annotation-only prompts.\\n\\nTitle: AncFlow: An Ancestral Sequence Reconstruction Approach for Determining Novel Protein Structural\\nSummary: Here is the summary in 2 sentences: AncFlow is an automated software pipeline that integrates phylogenetic analysis, subfamily identification, and ancestral sequence reconstruction (ASR) to generate ancestral protein sequences for structural prediction using state-of-the-art tools like AlphaFold. The pipeline was validated on two well-characterized protein families, providing insights into the evolutionary mechanisms underpinning functional diversification within these families and demonstrating its potential to guide protein engineering efforts.\""},{"path":"https://stephenturner.github.io/biorecap/reference/example_preprints.html","id":null,"dir":"Reference","previous_headings":"","what":"Example preprints with summaries — example_preprints","title":"Example preprints with summaries — example_preprints","text":"Example preprints summaries August 6, 2024.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/example_preprints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example preprints with summaries — example_preprints","text":"","code":"example_preprints"},{"path":"https://stephenturner.github.io/biorecap/reference/example_preprints.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example preprints with summaries — example_preprints","text":"tibble returned get_preprints() followed add_prompt() followed add_summary().","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/example_preprints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example preprints with summaries — example_preprints","text":"","code":"example_preprints #> # A tibble: 90 × 6 #> subject title url abstract prompt summary #> #> 1 bioinformatics Integrity and miss grouping as … http… \"The hi… \"I am… \"The p… #> 2 bioinformatics Sainsc: a computational tool fo… http… \"Spatia… \"I am… \"Sains… #> 3 bioinformatics BRACE: A novel Bayesian-based i… http… \"Bayesi… \"I am… \"Alter… #> 4 bioinformatics Topological embedding and direc… http… \"Cancer… \"I am… \"Resea… #> 5 bioinformatics SeuratExtend: Streamlining Sing… http… \"Single… \"I am… \"Seura… #> 6 bioinformatics An Evolutionary Statistics Tool… http… \"We pre… \"I am… \"The \\… #> 7 bioinformatics A map of integrated cis-regulat… http… \"Cis-re… \"I am… \"The a… #> 8 bioinformatics MOSTPLAS: A Self-correction Mul… http… \"Plasmi… \"I am… \"Plasm… #> 9 bioinformatics Bootstrap Evaluation of Associa… http… \"Motiva… \"I am… \"The a… #> 10 bioinformatics Thermodynamic modeling of Csr/R… http… \"Backgr… \"I am… \"Resea… #> # ℹ 80 more rows"},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":null,"dir":"Reference","previous_headings":"","what":"Get bioRxiv preprints — get_preprints","title":"Get bioRxiv preprints — get_preprints","text":"Get bioRxiv preprints","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get bioRxiv preprints — get_preprints","text":"","code":"get_preprints( subject = \"all\", baseurl = \"https://connect.biorxiv.org/biorxiv_xml.php?subject=\", clean = TRUE )"},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get bioRxiv preprints — get_preprints","text":"subject character vector valid biorxiv subjects. See subjects. baseurl base URL biorxiv RSS feed. Default https://connect.biorxiv.org/biorxiv_xml.php?subject=. change unless know . clean Logical; try strip graphical abstract information? TRUE, strips away text O_FIG C_FIG, words graphical abstract abstract text RSS feed.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get bioRxiv preprints — get_preprints","text":"data frame bioRxiv preprints.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get bioRxiv preprints — get_preprints","text":"","code":"preprints <- get_preprints(subject=c(\"bioinformatics\", \"genomics\")) preprints #> # A tibble: 60 × 4 #> subject title url abstract #> #> 1 bioinformatics Tesorai Search: Large pretrained model boosts … http… Current… #> 2 bioinformatics DIAMOND2GO: A rapid Gene Ontology assignment a… http… DIAMOND… #> 3 bioinformatics Multi-Modal Large Language Model Enables Prote… http… Predict… #> 4 bioinformatics Sawfish: Improving long-read structural varian… http… Motivat… #> 5 bioinformatics Towards Digital Quantification of Ploidy from … http… Abnorma… #> 6 bioinformatics Interdependent regulation of alternative splic… http… Alterna… #> 7 bioinformatics UnigeneFinder: An automated pipeline for gene … http… For mos… #> 8 bioinformatics Normalization of Single-cell RNA-seq Data Usin… http… Normali… #> 9 bioinformatics Phytochemical Analysis and Cytotoxic Effects o… http… Hepatoc… #> 10 bioinformatics mobileRNA: a tool for efficient analysis of mo… http… In plan… #> # ℹ 50 more rows"},{"path":"https://stephenturner.github.io/biorecap/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. ollamar list_models, test_connection","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":null,"dir":"Reference","previous_headings":"","what":"bioRxiv subjects — subjects","title":"bioRxiv subjects — subjects","text":"Names subjects RSS feeds biorXiv","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"bioRxiv subjects — subjects","text":"","code":"subjects"},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"bioRxiv subjects — subjects","text":"character vector","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"bioRxiv subjects — subjects","text":"https://www.biorxiv.org/alertsrss","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"bioRxiv subjects — subjects","text":"","code":"subjects #> [1] \"all\" #> [2] \"animal_behavior_and_cognition\" #> [3] \"biochemistry\" #> [4] \"bioengineering\" #> [5] \"bioinformatics\" #> [6] \"biophysics\" #> [7] \"cancer_biology\" #> [8] \"cell_biology\" #> [9] \"clinical_trials\" #> [10] \"developmental_biology\" #> [11] \"ecology\" #> [12] \"epidemiology\" #> [13] \"evolutionary_biology\" #> [14] \"genetics\" #> [15] \"genomics\" #> [16] \"immunology\" #> [17] \"microbiology\" #> [18] \"molecular_biology\" #> [19] \"neuroscience\" #> [20] \"paleontology\" #> [21] \"pathology\" #> [22] \"pharmacology_and_toxicology\" #> [23] \"plant_biology\" #> [24] \"scientific_communication_and_education\" #> [25] \"synthetic_biology\" #> [26] \"systems_biology\" #> [27] \"zoology\""},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a markdown table from prepreprint summaries — tt_preprints","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"Create markdown table prepreprint summaries","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"","code":"tt_preprints(preprints, cols = c(\"title\", \"summary\"), width = c(1, 3))"},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"preprints Output get_preprints() followed add_prompt() followed add_summary(). cols Columns display resulting markdown table. width Vector relative widths equal length(cols).","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"tinytable table.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"","code":"# Use built-in example data example_preprints #> # A tibble: 90 × 6 #> subject title url abstract prompt summary #> #> 1 bioinformatics Integrity and miss grouping as … http… \"The hi… \"I am… \"The p… #> 2 bioinformatics Sainsc: a computational tool fo… http… \"Spatia… \"I am… \"Sains… #> 3 bioinformatics BRACE: A novel Bayesian-based i… http… \"Bayesi… \"I am… \"Alter… #> 4 bioinformatics Topological embedding and direc… http… \"Cancer… \"I am… \"Resea… #> 5 bioinformatics SeuratExtend: Streamlining Sing… http… \"Single… \"I am… \"Seura… #> 6 bioinformatics An Evolutionary Statistics Tool… http… \"We pre… \"I am… \"The \\… #> 7 bioinformatics A map of integrated cis-regulat… http… \"Cis-re… \"I am… \"The a… #> 8 bioinformatics MOSTPLAS: A Self-correction Mul… http… \"Plasmi… \"I am… \"Plasm… #> 9 bioinformatics Bootstrap Evaluation of Associa… http… \"Motiva… \"I am… \"The a… #> 10 bioinformatics Thermodynamic modeling of Csr/R… http… \"Backgr… \"I am… \"Resea… #> # ℹ 80 more rows tt_preprints(example_preprints[1:2,]) #> #> +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ #> | title | summary | #> +=====================================================================================================================================================================================+=========================================================================================================================================================================================================================================================================================================================================================================================================================================================================================+ #> | [Integrity and miss grouping as support for clusters in agglomerative hierarchical methods: the R-package octopucs](http://biorxiv.org/cgi/content/short/2024.08.01.606070v1?rss=1) | The proposed method assesses cluster support throughout hierarchical analyses by compiling a consensus topology and using ecological concepts of reciprocal complementarities to define cluster integrity and contamination. This approach allows for building support for groups even when there is partial membership match after resampling, and was implemented in the R package octopucs, which showed robust detection of changes in group memberships compared to other methods. | #> +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ #> | [Sainsc: a computational tool for segmentation-free analysis of in-situ capture](http://biorxiv.org/cgi/content/short/2024.08.02.603879v1?rss=1) | Sainsc is a computational tool that enables segmentation-free analysis of spatially resolved transcriptomics data, allowing for accurate cell-type assignment at the subcellular level without requiring manual cell border delineation. The tool provides efficient processing of high-resolution spatial data and can generate maps of cell types with corresponding confidence scores, making it a valuable resource for biomedical researchers working with complex tissue samples. | #> +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+"}] +[{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to biorecap","title":"Contributing to biorecap","text":"outlines propose change biorecap. detailed discussion contributing tidyverse packages, please see development contributing guide code review principles.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to biorecap","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to biorecap","text":"want make bigger change, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed). See guide create great issue advice.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to biorecap","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"stephenturner/biorecap\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Bigger changes","what":"Code style","title":"Contributing to biorecap","text":"New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. use roxygen2, Markdown syntax, documentation. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to biorecap","text":"Please note biorecap project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://stephenturner.github.io/biorecap/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2024 Stephen Turner Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://stephenturner.github.io/biorecap/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Stephen Turner. Author, maintainer.","code":""},{"path":"https://stephenturner.github.io/biorecap/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Turner S (2024). biorecap: Retrieve summarize bioRxiv preprints local LLM using ollama. R package version 0.1.0, https://stephenturner.github.io/biorecap/.","code":"@Manual{, title = {biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama}, author = {Stephen Turner}, year = {2024}, note = {R package version 0.1.0}, url = {https://stephenturner.github.io/biorecap/}, }"},{"path":"https://stephenturner.github.io/biorecap/index.html","id":"biorecap-","dir":"","previous_headings":"","what":"Retrieve and summarize bioRxiv preprints with a local LLM using ollama","title":"Retrieve and summarize bioRxiv preprints with a local LLM using ollama","text":"Retrieve summarize bioRxiv preprints using local LLM Ollama via ollamar. Turner, S. D. (2024). biorecap: R package summarizing bioRxiv preprints local LLM. arXiv, 2408.11707. https://doi.org/10.48550/arXiv.2408.11707.","code":""},{"path":"https://stephenturner.github.io/biorecap/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Retrieve and summarize bioRxiv preprints with a local LLM using ollama","text":"Install biorecap GitHub (keep dependencies=TRUE get Suggests packages needed create HTML report):","code":"# install.packages(\"remotes\") remotes::install_github(\"stephenturner/biorecap\", dependencies=TRUE)"},{"path":[]},{"path":"https://stephenturner.github.io/biorecap/index.html","id":"quick-start","dir":"","previous_headings":"Usage","what":"Quick start","title":"Retrieve and summarize bioRxiv preprints with a local LLM using ollama","text":"First, load biorecap library. Let’s make sure Ollama running can talk R: Next can list available models: Write HTML report containing summaries recent preprints select subject areas current working directory. Example HTML report generated bioRxiv RSS feed August 6, 2024:","code":"library(biorecap) test_connection() #> Ollama local server running #> #> GET http://localhost:11434/ #> Status: 200 OK #> Content-Type: text/plain #> Body: In memory (17 bytes) list_models() #> # A tibble: 3 × 4 #> name model parameter_size quantization_level #> #> 1 gemma2:latest gemma2:latest 9.2B Q4_0 #> 2 llama3.1:latest llama3.1:latest 8.0B Q4_0 #> 3 llama3.1:70b llama3.1:70b 70.6B Q4_0 biorecap_report(output_dir=\".\", subject=c(\"bioinformatics\", \"genomics\", \"synthetic_biology\"), model=\"llama3.1\")"},{"path":"https://stephenturner.github.io/biorecap/index.html","id":"details","dir":"","previous_headings":"Usage","what":"Details","title":"Retrieve and summarize bioRxiv preprints with a local LLM using ollama","text":"get_preprints() function retrieves preprints bioRxiv’s RSS feeds. pass one subjects subject argument. add_prompt() function adds prompt preprint used prompt model. Let’s take look one prompts: giving paper’s title abstract. Summarize paper many sentences instruct. include preamble text. Just give summary. Number sentences summary: 2 Title: SeuratExtend: Streamlining Single-Cell RNA-Seq Analysis Integrated Intuitive Framework Abstract: Single-cell RNA sequencing (scRNA-seq) revolutionized study cellular heterogeneity, rapid expansion analytical tools proven blessing curse, presenting researchers significant challenges. , present SeuratExtend, comprehensive R package built upon widely adopted Seurat framework, streamlines scRNA-seq data analysis integrating essential tools databases. SeuratExtend offers user-friendly intuitive interface performing wide range analyses, including functional enrichment, trajectory inference, gene regulatory network reconstruction, denoising. package seamlessly integrates multiple databases, Gene Ontology Reactome, incorporates popular Python tools like scVelo, Palantir, SCENIC unified R interface. SeuratExtend enhances data visualization optimized plotting functions carefully curated color schemes, ensuring aesthetic appeal scientific rigor. demonstrate SeuratExtend’s performance case studies investigating tumor-associated high-endothelial venules autoinflammatory diseases, showcase novel applications pathway-Level analysis cluster annotation. SeuratExtend empowers researchers harness full potential scRNA-seq data, making complex analyses accessible wider audience. package, along comprehensive documentation tutorials, freely available GitHub, providing valuable resource single-cell genomics community. add_summary() function uses locally running LLM available Ollama summarize preprint. Let’s add summary. Notice can single pipeline. takes minutes! Let’s take look results: Let’s look one summaries. ’s summary SeuratExtend paper (abstract ): SeuratExtend R package integrates essential tools databases single-cell RNA sequencing (scRNA-seq) data analysis, streamlining process user-friendly interface. package offers various analyses, including functional enrichment gene regulatory network reconstruction, seamlessly integrates multiple databases popular Python tools. biorecap_report() function runs code RMarkdown template, writing resulting HTML CSV file results current working directory. built-subjects vector contains available bioRxiv subjects. create report subjects like (note, take time):","code":"pp <- get_preprints(subject=c(\"bioinformatics\", \"genomics\", \"synthetic_biology\")) pp #> # A tibble: 90 × 4 #> subject title url abstract #> #> 1 bioinformatics Integrity and miss grouping as support for clu… http… \"The hi… #> 2 bioinformatics Sainsc: a computational tool for segmentation-… http… \"Spatia… #> 3 bioinformatics BRACE: A novel Bayesian-based imputation appro… http… \"Bayesi… #> 4 bioinformatics Topological embedding and directional feature … http… \"Cancer… #> 5 bioinformatics SeuratExtend: Streamlining Single-Cell RNA-Seq… http… \"Single… #> 6 bioinformatics An Evolutionary Statistics Toolkit for Simplif… http… \"We pre… #> 7 bioinformatics A map of integrated cis-regulatory elements en… http… \"Cis-re… #> 8 bioinformatics MOSTPLAS: A Self-correction Multi-label Learni… http… \"Plasmi… #> 9 bioinformatics Bootstrap Evaluation of Association Matrices (… http… \"Motiva… #> 10 bioinformatics Thermodynamic modeling of Csr/Rsm- RNA interac… http… \"Backgr… #> # ℹ 80 more rows pp <- pp |> add_prompt() pp #> # A tibble: 90 × 5 #> subject title url abstract prompt #> #> 1 bioinformatics Integrity and miss grouping as support … http… \"The hi… \"I am… #> 2 bioinformatics Sainsc: a computational tool for segmen… http… \"Spatia… \"I am… #> 3 bioinformatics BRACE: A novel Bayesian-based imputatio… http… \"Bayesi… \"I am… #> 4 bioinformatics Topological embedding and directional f… http… \"Cancer… \"I am… #> 5 bioinformatics SeuratExtend: Streamlining Single-Cell … http… \"Single… \"I am… #> 6 bioinformatics An Evolutionary Statistics Toolkit for … http… \"We pre… \"I am… #> 7 bioinformatics A map of integrated cis-regulatory elem… http… \"Cis-re… \"I am… #> 8 bioinformatics MOSTPLAS: A Self-correction Multi-label… http… \"Plasmi… \"I am… #> 9 bioinformatics Bootstrap Evaluation of Association Mat… http… \"Motiva… \"I am… #> 10 bioinformatics Thermodynamic modeling of Csr/Rsm- RNA … http… \"Backgr… \"I am… #> # ℹ 80 more rows pp <- get_preprints(subject=c(\"bioinformatics\", \"genomics\", \"synthetic_biology\")) |> add_prompt() |> add_summary(model=\"llama3.1\") pp #> # A tibble: 90 × 6 #> subject title url abstract prompt summary #> #> 1 bioinformatics Integrity and miss grouping as … http… \"The hi… \"I am… \"The p… #> 2 bioinformatics Sainsc: a computational tool fo… http… \"Spatia… \"I am… \"Sains… #> 3 bioinformatics BRACE: A novel Bayesian-based i… http… \"Bayesi… \"I am… \"Alter… #> 4 bioinformatics Topological embedding and direc… http… \"Cancer… \"I am… \"Resea… #> 5 bioinformatics SeuratExtend: Streamlining Sing… http… \"Single… \"I am… \"Seura… #> 6 bioinformatics An Evolutionary Statistics Tool… http… \"We pre… \"I am… \"The \\… #> 7 bioinformatics A map of integrated cis-regulat… http… \"Cis-re… \"I am… \"The a… #> 8 bioinformatics MOSTPLAS: A Self-correction Mul… http… \"Plasmi… \"I am… \"Plasm… #> 9 bioinformatics Bootstrap Evaluation of Associa… http… \"Motiva… \"I am… \"The a… #> 10 bioinformatics Thermodynamic modeling of Csr/R… http… \"Backgr… \"I am… \"Resea… #> # ℹ 80 more rows biorecap_report(output_dir=\".\", subject=c(\"bioinformatics\", \"genomics\", \"synthetic_biology\"), model=\"llama3.1\") subjects #> [1] \"all\" #> [2] \"animal_behavior_and_cognition\" #> [3] \"biochemistry\" #> [4] \"bioengineering\" #> [5] \"bioinformatics\" #> [6] \"biophysics\" #> [7] \"cancer_biology\" #> [8] \"cell_biology\" #> [9] \"clinical_trials\" #> [10] \"developmental_biology\" #> [11] \"ecology\" #> [12] \"epidemiology\" #> [13] \"evolutionary_biology\" #> [14] \"genetics\" #> [15] \"genomics\" #> [16] \"immunology\" #> [17] \"microbiology\" #> [18] \"molecular_biology\" #> [19] \"neuroscience\" #> [20] \"paleontology\" #> [21] \"pathology\" #> [22] \"pharmacology_and_toxicology\" #> [23] \"plant_biology\" #> [24] \"scientific_communication_and_education\" #> [25] \"synthetic_biology\" #> [26] \"systems_biology\" #> [27] \"zoology\" biorecap_report(output_dir=\".\", subject=subjects, model=\"llama3.1\")"},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":null,"dir":"Reference","previous_headings":"","what":"Add prompt to a data frame of preprints — add_prompt","title":"Add prompt to a data frame of preprints — add_prompt","text":"Add prompt data frame preprints","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add prompt to a data frame of preprints — add_prompt","text":"","code":"add_prompt(preprints, ...)"},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add prompt to a data frame of preprints — add_prompt","text":"preprints Result get_preprints(). ... Additional arguments build_prompt_preprint().","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add prompt to a data frame of preprints — add_prompt","text":"data frame bioRxiv preprints prompt added.","code":""},{"path":[]},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add prompt to a data frame of preprints — add_prompt","text":"","code":"preprints <- get_preprints(subject=c(\"bioinformatics\", \"genomics\")) preprints <- add_prompt(preprints) preprints #> # A tibble: 60 × 5 #> subject title url abstract prompt #> #> 1 bioinformatics Unsupervised domain classification of A… http… The rel… \"I am… #> 2 bioinformatics Testing and overcoming the limitations … http… Modular… \"I am… #> 3 bioinformatics Decoding multicellular niche formation … http… Accurat… \"I am… #> 4 bioinformatics Insights from Molecular Docking and Dyn… http… Alpha-s… \"I am… #> 5 bioinformatics TUSCAN: Tumor segmentation and classifi… http… The ide… \"I am… #> 6 bioinformatics Characterizing the role of exosomal miR… http… Backgro… \"I am… #> 7 bioinformatics The Lomb-Scargle periodogram-based diff… http… Motivat… \"I am… #> 8 bioinformatics Protein stability models fail to captur… http… There i… \"I am… #> 9 bioinformatics CryptoBench: Cryptic protein-ligand bin… http… Structu… \"I am… #> 10 bioinformatics haCCA: Multi-module Integrating of spat… http… Spatial… \"I am… #> # ℹ 50 more rows"},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":null,"dir":"Reference","previous_headings":"","what":"Add prompts for an entire subject — add_prompt_subject","title":"Add prompts for an entire subject — add_prompt_subject","text":"Add prompts entire subject","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add prompts for an entire subject — add_prompt_subject","text":"","code":"add_prompt_subject(preprints, ...)"},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add prompts for an entire subject — add_prompt_subject","text":"preprints Output get_preprints() followed add_prompt() followed add_summary(). ... Additional arguments build_prompt_subject().","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add prompts for an entire subject — add_prompt_subject","text":"tibble subject prompt column.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add prompts for an entire subject — add_prompt_subject","text":"","code":"subjects <- example_preprints |> dplyr::group_by(subject) |> add_prompt_subject() #> Warning: Expecting a tibble of class 'preprints_prompt' returned from get_preprints() |> add_prompt(). subjects #> # A tibble: 3 × 2 #> subject prompt #> #> 1 bioinformatics \"I am giving you information about preprints published in b… #> 2 genomics \"I am giving you information about preprints published in b… #> 3 synthetic_biology \"I am giving you information about preprints published in b…"},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a summary from a data frame of prompts — add_summary","title":"Generate a summary from a data frame of prompts — add_summary","text":"Generate summary data frame prompts","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a summary from a data frame of prompts — add_summary","text":"","code":"add_summary(preprints, model = \"llama3.1\")"},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a summary from a data frame of prompts — add_summary","text":"preprints Output get_preprints() followed add_prompt(). model model available Ollama (run ollamar::list_models()) see available.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a summary from a data frame of prompts — add_summary","text":"tibble, response column added.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a summary from a data frame of prompts — add_summary","text":"","code":"if (FALSE) { # \\dontrun{ # Individual papers preprints <- get_preprints(c(\"genomics\", \"bioinformatics\")) |> add_prompt() |> add_summary() preprints } # }"},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap-package.html","id":null,"dir":"Reference","previous_headings":"","what":"biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama — biorecap-package","title":"biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama — biorecap-package","text":"Retrieve summarize bioRxiv preprints local LLM using ollama.","code":""},{"path":[]},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama — biorecap-package","text":"Maintainer: Stephen Turner vustephen@gmail.com (ORCID)","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a report from bioRxiv preprints — biorecap_report","title":"Create a report from bioRxiv preprints — biorecap_report","text":"Create report bioRxiv preprints","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a report from bioRxiv preprints — biorecap_report","text":"","code":"biorecap_report( output_dir = \".\", subject = NULL, nsentences = 2L, model = \"llama3.1\", use_example_preprints = FALSE, ... )"},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a report from bioRxiv preprints — biorecap_report","text":"output_dir Directory save report. subject Character vector subjects include report. nsentences Number sentences summarize paper . model model use generating summaries. See ollamar::list_models(). use_example_preprints Use example preprints data included package instead fetching new data bioRxiv. diagnostic/testing purposes . ... arguments passed rmarkdown::render().","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a report from bioRxiv preprints — biorecap_report","text":"Nothing; called side effects produce report.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a report from bioRxiv preprints — biorecap_report","text":"","code":"if (FALSE) { # \\dontrun{ output_dir <- tempdir() biorecap_report(use_example_preprints=TRUE, output_dir=output_dir) biorecap_report(subject=c(\"bioinformatics\", \"genomics\", \"synthetic_biology\"), output_dir=output_dir) } # }"},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct a prompt to summarize a paper — build_prompt_preprint","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"Construct prompt summarize paper","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"","code":"build_prompt_preprint( title, abstract, nsentences = 2L, instructions = c(\"I am giving you a paper's title and abstract.\", \"Summarize the paper in as many sentences as I instruct.\", \"Do not include any preamble text to the summary\", \"just give me the summary with no preface or intro sentence.\") )"},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"title title paper. abstract abstract paper. nsentences number sentences summarize paper . instructions Instructions prompt. can character vector gets collapsed single string.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"string containing prompt.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"","code":"build_prompt_preprint(title=\"A great paper\", abstract=\"This is the abstract.\") #> [1] \"I am giving you a paper's title and abstract. Summarize the paper in as many sentences as I instruct. Do not include any preamble text to the summary just give me the summary with no preface or intro sentence.\\nNumber of sentences in summary: 2\\nTitle: A great paper\\nAbstract: This is the abstract.\""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"Construct prompt summarize set papers subject","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"","code":"build_prompt_subject( subject, title, summary, nsentences = 5L, instructions = c(\"I am giving you information about preprints published in bioRxiv recently.\", \"I'll give you the subject, preprint titles, and short summary of each paper.\", \"Please provide a general summary new advances in this subject/field in general.\", \"Provide this summary of the field in as many sentences as I instruct.\", \"Do not include any preamble text to the summary\", \"just give me the summary with no preface or intro sentence.\") )"},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"subject name subject. title character vector titles subject summary character vector summaries paper provided get_preprints() followed add_prompt() followed add_summary(). nsentences number sentences summarize subject . instructions Instructions prompt. can character vector gets collapsed single string.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"string containing prompt.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"","code":"title <- example_preprints |> dplyr::filter(subject==\"bioinformatics\") |> dplyr::pull(title) summary <- example_preprints |> dplyr::filter(subject==\"bioinformatics\") |> dplyr::pull(summary) build_prompt_subject(subject=\"bioinformatics\", title=title, summary=summary) #> [1] \"I am giving you information about preprints published in bioRxiv recently. I'll give you the subject, preprint titles, and short summary of each paper. Please provide a general summary new advances in this subject/field in general. Provide this summary of the field in as many sentences as I instruct. Do not include any preamble text to the summary just give me the summary with no preface or intro sentence.\\n\\nSubject: bioinformatics\\nNumber of sentences in summary: 5\\n\\nHere are the titles and summaries:\\n\\nTitle: Integrity and miss grouping as support for clusters in agglomerative hierarchical methods: the R-package octopucs\\nSummary: The proposed method assesses cluster support throughout hierarchical analyses by compiling a consensus topology and using ecological concepts of reciprocal complementarities to define cluster integrity and contamination. This approach allows for building support for groups even when there is partial membership match after resampling, and was implemented in the R package octopucs, which showed robust detection of changes in group memberships compared to other methods.\\n\\nTitle: Sainsc: a computational tool for segmentation-free analysis of in-situ capture\\nSummary: Sainsc is a computational tool that enables segmentation-free analysis of spatially resolved transcriptomics data, allowing for accurate cell-type assignment at the subcellular level without requiring manual cell border delineation. The tool provides efficient processing of high-resolution spatial data and can generate maps of cell types with corresponding confidence scores, making it a valuable resource for biomedical researchers working with complex tissue samples.\\n\\nTitle: BRACE: A novel Bayesian-based imputation approach for dimension reduction analysis of alternative splicing at single-cell resolution\\nSummary: Alternative splicing represents an additional layer of complexity in gene expression profiles, but analyzing it at single-cell resolution is challenging due to missing data. This paper introduces BRACE, a Bayesian-based imputation approach that improves upon existing methods and enables dimension reduction analysis of alternative splicing events at single-cell resolution.\\n\\nTitle: Topological embedding and directional feature importance in ensemble classifiers for multi-class classification\\nSummary: Researchers developed a new metric called class-based direction feature importance (CLIFI) to provide interpretable insights into the decision-making process of ensemble classifiers for multi-class classification problems, specifically in the context of cancer biomarker identification. The CLIFI metric was incorporated into four algorithms and applied to The Cancer Genome Atlas proteomics data, resulting in high F1-scores and allowing for the visualization of model decision-making functions and the identification of heterogeneity in several proteins across different cancer types.\\n\\nTitle: SeuratExtend: Streamlining Single-Cell RNA-Seq Analysis Through an Integrated and Intuitive Framework\\nSummary: SeuratExtend is an R package that integrates essential tools and databases for single-cell RNA sequencing (scRNA-seq) data analysis, streamlining the process through a user-friendly interface. The package offers various analyses, including functional enrichment and gene regulatory network reconstruction, and seamlessly integrates multiple databases and popular Python tools.\\n\\nTitle: An Evolutionary Statistics Toolkit for Simplified Sequence Analysis on Web with Client-Side Processing\\nSummary: The \\\"Evolutionary Statistics Toolkit\\\" is a web-based platform that integrates multiple evolutionary statistics tools for simplified sequence analysis, including Tajima's D calculator and Shannon's Entropy. The open-source toolkit facilitates streamlined workflows for researchers in evolutionary biology and genomics, and also serves as an educational interactive website for beginners in evolutionary statistics.\\n\\nTitle: A map of integrated cis-regulatory elements enhances gene regulatory analysis in maize\\nSummary: The authors integrated various methods for profiling cis-regulatory elements (CREs) in maize, resulting in maps of integrated CREs that show increased completeness and precision. These maps were used to infer drought-specific gene regulatory networks and identify candidate regulators of maize drought response, as well as to study the potential role of transposable elements in regulating gene expression.\\n\\nTitle: MOSTPLAS: A Self-correction Multi-label Learning Model for Plasmid Host Range Prediction\\nSummary: Plasmid host range prediction tools are essential for understanding how plasmids promote bacterial evolution, but existing learning-based tools struggle due to limited well-annotated training samples. The proposed model, MOSTPLAS, addresses this issue with a self-correction multi-label learning approach that uses pseudo label learning and asymmetric loss to facilitate training with incomplete labels.\\n\\nTitle: Bootstrap Evaluation of Association Matrices (BEAM) for Integrating Multiple Omics Profiles with Multiple Outcomes\\nSummary: The authors propose Bootstrap Evaluation of Association Matrices (BEAM), a new statistical method that integrates multiple omics profiles with multiple clinical endpoints to identify significant associations between them. BEAM outperformed other integrated analysis methods in simulations and identified biologically relevant genes in a pediatric leukemia application that were missed by univariate screens and other methods.\\n\\nTitle: Thermodynamic modeling of Csr/Rsm- RNA interactions capture novel, direct binding interactions across the Pseudomonas aeruginosa transcriptome\\nSummary: Researchers developed a thermodynamic model to predict interactions between the post-transcriptional regulator RsmA and mRNAs in Pseudomonas aeruginosa, predicting 1043 direct binding interactions, including 457 novel targets. The predictions were validated through in vitro binding assays and in vivo translational reporters, revealing direct regulation of genes involved in quorum sensing and the Type IV Secretion system, expanding the known pool of RsmA target genes.\\n\\nTitle: Assessing the ability of ChatGPT to extract natural product bioactivity and biosynthesis data from publications\\nSummary: ChatGPT was tested on its ability to extract data from publications on natural product bioactivity and biosynthesis, which is crucial for training models that predict natural product activity from biosynthetic gene clusters. The results showed that ChatGPT performed well in identifying papers describing natural product discovery and extracting information about the product's bioactivity, but struggled with extracting accession numbers for the biosynthetic gene cluster or producer's genome.\\n\\nTitle: Genome-Wide Analysis of TCP Family Genes and Their Constitutive Expression Pattern Analysis in the Melon (Cucumis melo)\\nSummary: This study identified and characterized 29 putative TCP genes in melon, classifying them into two classes and analyzing their chromosomal location, gene structure, and expression patterns. The results suggest that some CmTCP genes may have similar functions to their homologs in other plant species, while others may have undergone functional diversification, providing a resource for future investigations into their roles in melon development.\\n\\nTitle: Single-cell differential expression analysis between conditions within nested settings\\nSummary: Researchers compared various methods for differential expression analysis of single-cell transcriptomics data and found that methods designed specifically for single-cell data do not offer performance advantages over conventional pseudobulk methods like DESeq2 when applied to individual datasets. However, permutation-based methods excel in performance for atlas-level analysis, but require significantly longer run times, making DREAM a compromise between quality and runtime.\\n\\nTitle: CoMPHI: A Novel Composite Machine Learning Approach Utilizing Multiple FeatureRepresentation to Predict Hosts of Bacteriophages\\nSummary: Here is a 2-sentence summary of the paper: This study introduces CoMPHI, a novel composite machine learning approach that combines multiple feature representations to predict hosts of bacteriophages, with potential applications in phage therapy for treating bacterial infections. The model achieves high prediction accuracy, with an Area Under the ROC Curve (AUC) of up to 96.7% and accuracy of up to 95.1%, outperforming existing methods due to its inclusion of alignment scores and use of both nucleotide and protein sequences from phages and hosts.\\n\\nTitle: FourierMIL: Fourier filtering-based multiple instance learning for whole slide image analysis\\nSummary: The paper presents FourierMIL, a multiple instance learning framework that uses the discrete Fourier transform to analyze whole-slide images (WSIs) in digital pathology. The method captures both global and local dependencies within WSIs and outperforms existing state-of-the-art methods in tumor classification tasks on gigapixel-resolution WSIs.\\n\\nTitle: Multiple Protein Structure Alignment at Scale with FoldMason\\nSummary: Here is a 2-sentence summary of the paper: FoldMason is a new method for multiple protein structure alignment that can handle hundreds of thousands of structures at scale with high speed and accuracy. It leverages the structural alphabet from Foldseek to compute confidence scores, provide interactive visualizations, and support large-scale protein structure analysis and phylogenetic studies.\\n\\nTitle: Deciphering octoploid strawberry evolution with serial LTR similarity matrices for subgenome partition\\nSummary: A novel approach was developed to assign polyploid genome assemblies to subgenomes using long terminal repeat retrotransposons (LTR-RTs) and the Serial Similarity Matrix (SSM) method, which is particularly useful for genomes without known diploid ancestors. The SSM approach was validated using well-studied allopolyploidy genomes and then applied to the octoploid strawberry genome, revealing three allopolyploidization events in its evolutionary history.\\n\\nTitle: IDENTIFICATION OF IMMUNE RESPONSE AND RNA NETWORK OF RHEUMATOID ARTHRITIS AND MOLECULAR DOCKING OF CELASTRUS PANICULATUS AS POTENTIAL THERAPEUTIC AGENT\\nSummary: This study used bioinformatics analysis to identify immune responses, microRNA-hub genes networks, and potential therapeutic agents for rheumatoid arthritis (RA), a complex autoimmune disease with an unknown pathogenesis. The researchers found several hub genes and miRNAs associated with RA, and identified oleic acid and zeylasterone as potential novel drug candidates against the disease through molecular docking analysis of Celastrus paniculatus phytochemical compounds.\\n\\nTitle: Imputing abundance of over 2500 surface proteins from single-cell transcriptomes with context-agnostic zero-shot deep ensembles\\nSummary: SPIDER is a deep ensemble model that predicts the abundance of over 2500 surface proteins from single-cell transcriptomes with improved generalization across diverse contexts such as tissues or disease states. The model outperforms other state-of-the-art methods and has various applications including cell type annotation, biomarker/target identification, and cell-cell interaction analysis in cancer research.\\n\\nTitle: Modelling Protein-Glycan Interactions with HADDOCK\\nSummary: Glycans play important roles in living organisms by interacting with proteins for information transfer and signalling purposes, making it essential to determine the three-dimensional structures of protein-glycan complexes. The molecular docking approach HADDOCK was used to predict protein-glycan complexes with a top 5 success rate of 70% for bound datasets and 40% for unbound datasets using a benchmark of 89 complexes.\\n\\nTitle: Machine Learning Reveals Key Glycoprotein Mutations and Rapidly Assigns Lassa Virus Lineages\\nSummary: Machine learning and phylogenetic analysis of Lassa virus glycoprotein sequences revealed key mutations and genetic differences between Nigerian lineages and those from other West African countries. The study identified specific amino acid positions that are highly variable among the lineages, which may explain structural and phenotypical differences, and developed a machine learning-based tool for rapid lineage classification.\\n\\nTitle: RESP2: An uncertainty aware multi-target multi-property optimization AI pipeline for antibody discovery\\nSummary: The RESP2 pipeline is an AI-powered tool designed to optimize the discovery of therapeutic antibodies against infectious disease pathogens, taking into account multiple targets and properties such as specificity, low immunogenicity, and high affinity. The pipeline uses a suite of methods to estimate uncertainty in predictions and has been successfully applied to discover a highly human antibody with broad binding to variants of the COVID-19 spike protein receptor binding domain.\\n\\nTitle: Extending the capabilities of deconvolution to provide cell type specific pathway analysis of bulk RNA-seq data for idiopathic pulmonary fibrosis\\nSummary: A deconvolution method was applied to bulk RNA-seq data from idiopathic pulmonary fibrosis (IPF) samples to correct for changes in cell type proportions and provide cell-type specific pathway analysis. The results showed significant increases in fibroblasts and myofibroblasts, decreases in vascular endothelial capillary cells, and IPF-related changes in extracellular matrix organization and TGF-{beta} regulation, as well as the involvement of interferon signaling in ATII cells.\\n\\nTitle: A survey of ADP-ribosyltransferase families in the pathogenic Legionella\\nSummary: A comprehensive bioinformatic survey of 41 Legionella species identified 63 proteins with significant sequence or structural similarity to known ADP-ribosyltransferases (ARTs), organized into 39 ART-like families, including 26 novel families. The study found that most members of the novel ART families are predicted effectors, presenting promising targets for understanding Legionella pathogenicity and developing therapeutic strategies.\\n\\nTitle: A replicable and modular benchmark for long-read transcript quantification methods\\nSummary: Researchers have developed a replicable benchmark for evaluating long-read transcript quantification methods using synthetic RNA-seq datasets, which can be easily extended to include new tools or data sets. The study reveals discrepancies with previously published results and highlights the importance of high-quality simulated data in assessing the robustness of certain approaches.\\n\\nTitle: Logan: Planetary-Scale Genome Assembly Surveys Life's Diversity\\nSummary: The NCBI Sequence Read Archive contains over 50 petabases of DNA sequencing data across 27 million datasets, but its size makes it impractical to search for specific genetic sequences within a reasonable time frame. To address this issue, the authors used cloud computing to perform genome assembly on each dataset and created the Logan assemblage, which is now freely available and enables faster querying of the data, with some queries completing in as little as 11 hours.\\n\\nTitle: Cell-type specific epigenetic clocks to quantify biological age at cell-type resolution\\nSummary: Epigenetic clocks have been developed to estimate biological age, but most are based on heterogeneous bulk tissues and reflect both changes in cell-type composition and individual cell aging. This study created neuron- and hepatocyte-specific DNA methylation clocks that provide improved estimates of chronological age and detect accelerated biological aging in Alzheimer's disease and liver pathology.\\n\\nTitle: Genomic and transcriptomic analyses of Heteropoda venatoria reveal the expansion of P450 family for starvation resistance in spider\\nSummary: The genome of Heteropoda venatoria was sequenced and comparative genomic analysis revealed significant expansions in gene families related to lipid metabolism, including cytochrome P450 and steroid hormone biosynthesis genes. The study found that during starvation, H. venatoria undergoes a series of physiological changes, including the activation of fatty acid metabolism and protein degradation pathways, and the expression of expanded P450 gene families, which help the spider maintain a low-energy metabolic state and endure longer periods of starvation.\\n\\nTitle: Annotation Vocabulary (Might Be) All You Need\\nSummary: The authors introduce the \\\"Annotation Vocabulary\\\", a language of protein properties defined by structured ontologies that can be used to train transformer models without reference to amino acid sequences. They demonstrate the effectiveness of this approach in various experiments, achieving state-of-the-art results on several common datasets with competitive performance on others, and generating high-quality de novo protein sequences from annotation-only prompts.\\n\\nTitle: AncFlow: An Ancestral Sequence Reconstruction Approach for Determining Novel Protein Structural\\nSummary: Here is the summary in 2 sentences: AncFlow is an automated software pipeline that integrates phylogenetic analysis, subfamily identification, and ancestral sequence reconstruction (ASR) to generate ancestral protein sequences for structural prediction using state-of-the-art tools like AlphaFold. The pipeline was validated on two well-characterized protein families, providing insights into the evolutionary mechanisms underpinning functional diversification within these families and demonstrating its potential to guide protein engineering efforts.\""},{"path":"https://stephenturner.github.io/biorecap/reference/example_preprints.html","id":null,"dir":"Reference","previous_headings":"","what":"Example preprints with summaries — example_preprints","title":"Example preprints with summaries — example_preprints","text":"Example preprints summaries August 6, 2024.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/example_preprints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example preprints with summaries — example_preprints","text":"","code":"example_preprints"},{"path":"https://stephenturner.github.io/biorecap/reference/example_preprints.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example preprints with summaries — example_preprints","text":"tibble returned get_preprints() followed add_prompt() followed add_summary().","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/example_preprints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example preprints with summaries — example_preprints","text":"","code":"example_preprints #> # A tibble: 90 × 6 #> subject title url abstract prompt summary #> #> 1 bioinformatics Integrity and miss grouping as … http… \"The hi… \"I am… \"The p… #> 2 bioinformatics Sainsc: a computational tool fo… http… \"Spatia… \"I am… \"Sains… #> 3 bioinformatics BRACE: A novel Bayesian-based i… http… \"Bayesi… \"I am… \"Alter… #> 4 bioinformatics Topological embedding and direc… http… \"Cancer… \"I am… \"Resea… #> 5 bioinformatics SeuratExtend: Streamlining Sing… http… \"Single… \"I am… \"Seura… #> 6 bioinformatics An Evolutionary Statistics Tool… http… \"We pre… \"I am… \"The \\… #> 7 bioinformatics A map of integrated cis-regulat… http… \"Cis-re… \"I am… \"The a… #> 8 bioinformatics MOSTPLAS: A Self-correction Mul… http… \"Plasmi… \"I am… \"Plasm… #> 9 bioinformatics Bootstrap Evaluation of Associa… http… \"Motiva… \"I am… \"The a… #> 10 bioinformatics Thermodynamic modeling of Csr/R… http… \"Backgr… \"I am… \"Resea… #> # ℹ 80 more rows"},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":null,"dir":"Reference","previous_headings":"","what":"Get bioRxiv preprints — get_preprints","title":"Get bioRxiv preprints — get_preprints","text":"Get bioRxiv preprints","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get bioRxiv preprints — get_preprints","text":"","code":"get_preprints( subject = \"all\", baseurl = \"https://connect.biorxiv.org/biorxiv_xml.php?subject=\", clean = TRUE )"},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get bioRxiv preprints — get_preprints","text":"subject character vector valid biorxiv subjects. See subjects. baseurl base URL biorxiv RSS feed. Default https://connect.biorxiv.org/biorxiv_xml.php?subject=. change unless know . clean Logical; try strip graphical abstract information? TRUE, strips away text O_FIG C_FIG, words graphical abstract abstract text RSS feed.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get bioRxiv preprints — get_preprints","text":"data frame bioRxiv preprints.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get bioRxiv preprints — get_preprints","text":"","code":"preprints <- get_preprints(subject=c(\"bioinformatics\", \"genomics\")) preprints #> # A tibble: 60 × 4 #> subject title url abstract #> #> 1 bioinformatics Unsupervised domain classification of AlphaFol… http… The rel… #> 2 bioinformatics Testing and overcoming the limitations of Modu… http… Modular… #> 3 bioinformatics Decoding multicellular niche formation in the … http… Accurat… #> 4 bioinformatics Insights from Molecular Docking and Dynamics S… http… Alpha-s… #> 5 bioinformatics TUSCAN: Tumor segmentation and classification … http… The ide… #> 6 bioinformatics Characterizing the role of exosomal miRNAs in … http… Backgro… #> 7 bioinformatics The Lomb-Scargle periodogram-based differentia… http… Motivat… #> 8 bioinformatics Protein stability models fail to capture epist… http… There i… #> 9 bioinformatics CryptoBench: Cryptic protein-ligand binding si… http… Structu… #> 10 bioinformatics haCCA: Multi-module Integrating of spatial tra… http… Spatial… #> # ℹ 50 more rows"},{"path":"https://stephenturner.github.io/biorecap/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. ollamar list_models, test_connection","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":null,"dir":"Reference","previous_headings":"","what":"bioRxiv subjects — subjects","title":"bioRxiv subjects — subjects","text":"Names subjects RSS feeds biorXiv","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"bioRxiv subjects — subjects","text":"","code":"subjects"},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"bioRxiv subjects — subjects","text":"character vector","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"bioRxiv subjects — subjects","text":"https://www.biorxiv.org/alertsrss","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"bioRxiv subjects — subjects","text":"","code":"subjects #> [1] \"all\" #> [2] \"animal_behavior_and_cognition\" #> [3] \"biochemistry\" #> [4] \"bioengineering\" #> [5] \"bioinformatics\" #> [6] \"biophysics\" #> [7] \"cancer_biology\" #> [8] \"cell_biology\" #> [9] \"clinical_trials\" #> [10] \"developmental_biology\" #> [11] \"ecology\" #> [12] \"epidemiology\" #> [13] \"evolutionary_biology\" #> [14] \"genetics\" #> [15] \"genomics\" #> [16] \"immunology\" #> [17] \"microbiology\" #> [18] \"molecular_biology\" #> [19] \"neuroscience\" #> [20] \"paleontology\" #> [21] \"pathology\" #> [22] \"pharmacology_and_toxicology\" #> [23] \"plant_biology\" #> [24] \"scientific_communication_and_education\" #> [25] \"synthetic_biology\" #> [26] \"systems_biology\" #> [27] \"zoology\""},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a markdown table from prepreprint summaries — tt_preprints","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"Create markdown table prepreprint summaries","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"","code":"tt_preprints(preprints, cols = c(\"title\", \"summary\"), width = c(1, 3))"},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"preprints Output get_preprints() followed add_prompt() followed add_summary(). cols Columns display resulting markdown table. width Vector relative widths equal length(cols).","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"tinytable table.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"","code":"# Use built-in example data example_preprints #> # A tibble: 90 × 6 #> subject title url abstract prompt summary #> #> 1 bioinformatics Integrity and miss grouping as … http… \"The hi… \"I am… \"The p… #> 2 bioinformatics Sainsc: a computational tool fo… http… \"Spatia… \"I am… \"Sains… #> 3 bioinformatics BRACE: A novel Bayesian-based i… http… \"Bayesi… \"I am… \"Alter… #> 4 bioinformatics Topological embedding and direc… http… \"Cancer… \"I am… \"Resea… #> 5 bioinformatics SeuratExtend: Streamlining Sing… http… \"Single… \"I am… \"Seura… #> 6 bioinformatics An Evolutionary Statistics Tool… http… \"We pre… \"I am… \"The \\… #> 7 bioinformatics A map of integrated cis-regulat… http… \"Cis-re… \"I am… \"The a… #> 8 bioinformatics MOSTPLAS: A Self-correction Mul… http… \"Plasmi… \"I am… \"Plasm… #> 9 bioinformatics Bootstrap Evaluation of Associa… http… \"Motiva… \"I am… \"The a… #> 10 bioinformatics Thermodynamic modeling of Csr/R… http… \"Backgr… \"I am… \"Resea… #> # ℹ 80 more rows tt_preprints(example_preprints[1:2,]) #> #> +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ #> | title | summary | #> +=====================================================================================================================================================================================+=========================================================================================================================================================================================================================================================================================================================================================================================================================================================================================+ #> | [Integrity and miss grouping as support for clusters in agglomerative hierarchical methods: the R-package octopucs](http://biorxiv.org/cgi/content/short/2024.08.01.606070v1?rss=1) | The proposed method assesses cluster support throughout hierarchical analyses by compiling a consensus topology and using ecological concepts of reciprocal complementarities to define cluster integrity and contamination. This approach allows for building support for groups even when there is partial membership match after resampling, and was implemented in the R package octopucs, which showed robust detection of changes in group memberships compared to other methods. | #> +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ #> | [Sainsc: a computational tool for segmentation-free analysis of in-situ capture](http://biorxiv.org/cgi/content/short/2024.08.02.603879v1?rss=1) | Sainsc is a computational tool that enables segmentation-free analysis of spatially resolved transcriptomics data, allowing for accurate cell-type assignment at the subcellular level without requiring manual cell border delineation. The tool provides efficient processing of high-resolution spatial data and can generate maps of cell types with corresponding confidence scores, making it a valuable resource for biomedical researchers working with complex tissue samples. | #> +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+"}]