recount workflow: accessing over 70,000 human RNA-seq samples with Bioconductor

Abstract

The recount2 resource is composed of over 70,000 uniformly processed human RNA-seq samples spanning TCGA and SRA, including GTEx. The processed data can be accessed via the recount2 website and the recount Bioconductor package. This workflow explains in detail how to use the recount package and how to integrate it with other Bioconductor packages for several analyses that can be carried out with the recount2 resource. In particular, we describe how the coverage count matrices were computed in recount2 as well as different ways of obtaining public metadata, which can facilitate downstream analyses. Step-by-step directions show how to do a gene level differential expression analysis, visualize base-level genome coverage data, and perform an analyses at multiple feature levels. This workflow thus provides further information to understand the data in recount2 and a compendium of R code to use the data.

Workflow locations

The workflow is available on Bioconductor at https://www.bioconductor.org/help/workflows/recountWorkflow/ and F1000Research at https://f1000research.com/articles/6-1558/v1.

Documentation

For more information about recountWorkflow check the vignettes through Bioconductor or at the documentation website.

Installation instructions

Get the latest stable R release from CRAN. Then install recountWorkflow from Bioconductor using the following code:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install("recountWorkflow")

Citation

Below is the citation output from using citation('recountWorkflow') in R. Please run this yourself to check for any updates on how to cite recountWorkflow.

print(citation("recountWorkflow"), bibtex = TRUE)
#> To cite package 'recountWorkflow' in publications use:
#> 
#>   Collado-Torres L, Nellore A, Jaffe AE (2017). "recount workflow:
#>   Accessing over 70,000 human RNA-seq samples with Bioconductor
#>   [version 1; referees: 1 approved, 2 approved with reservations]."
#>   _F1000Research_. doi:10.12688/f1000research.12223.1
#>   <https://doi.org/10.12688/f1000research.12223.1>,
#>   <https://f1000research.com/articles/6-1558/v1>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     title = {recount workflow: Accessing over 70,000 human RNA-seq samples with Bioconductor [version 1; referees: 1 approved, 2 approved with reservations]},
#>     author = {Leonardo Collado-Torres and Abhinav Nellore and Andrew E. Jaffe},
#>     year = {2017},
#>     journal = {F1000Research},
#>     doi = {10.12688/f1000research.12223.1},
#>     url = {https://f1000research.com/articles/6-1558/v1},
#>   }
#> 
#>   Collado-Torres L, Nellore A, Jaffe AE (2023). _recount workflow:
#>   accessing over 70,000 human RNA-seq samples with Bioconductor_.
#>   doi:10.18129/B9.bioc.recountWorkflow
#>   <https://doi.org/10.18129/B9.bioc.recountWorkflow>,
#>   https://github.com/LieberInstitute/recountWorkflow - R package
#>   version 1.25.0,
#>   <http://www.bioconductor.org/packages/recountWorkflow>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {recount workflow: accessing over 70,000 human RNA-seq samples with Bioconductor},
#>     author = {Leonardo Collado-Torres and Abhinav Nellore and Andrew E. Jaffe},
#>     year = {2023},
#>     url = {http://www.bioconductor.org/packages/recountWorkflow},
#>     note = {https://github.com/LieberInstitute/recountWorkflow - R package version 1.25.0},
#>     doi = {10.18129/B9.bioc.recountWorkflow},
#>   }

Please note that the recountWorkflow was only made possible thanks to many other R and bioinformatics software authors, which are cited either in the vignettes and/or the paper(s) describing this package.

Code of Conduct

Please note that the recountWorkflow project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Development tools

Continuous code testing is possible thanks to GitHub actions through usethis, remotes, sysreqs and rcmdcheck customized to use Bioconductor’s docker containers and BiocCheck.
Code coverage assessment is possible thanks to codecov and covr.
The documentation website is automatically updated thanks to pkgdown.
The code is styled automatically thanks to styler.
The documentation is formatted thanks to devtools and roxygen2.