ARMOR: An Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq Data. Issue 7 (1st July 2019)

• Record Type: Journal Article
• Authors: Orjuela, Stephany; Huang, Ruizhu; Hembach, Katharina M; Robinson, Mark D; Soneson, Charlotte
• Is Part Of: G3. Volume 9:Issue 7(2019)
• Page Start: 2089
• Page End: 2096
• Publication Date: 2019-07-01
• Subjects: RNA sequencing -- Differential expression -- Exploratory data analysis -- Quality control; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1534/g3.119.400185
• Languages: English
• ISSNs: 2160-1836

Abstract

Abstract: The extensive generation of RNA sequencing (RNA-seq) data in the last decade has resulted in a myriad of specialized software for its analysis. Each software module typically targets a specific step within the analysis pipeline, making it necessary to join several of them to get a single cohesive workflow. Multiple software programs automating this procedure have been proposed, but often lack modularity, transparency or flexibility. We present ARMOR, which performs an end-to-end RNA-seq data analysis, from raw read files, via quality checks, alignment and quantification, to differential expression testing, geneset analysis and browser-based exploration of the data. ARMOR is implemented using the Snakemake workflow management system and leverages conda environments; Bioconductor objects are generated to facilitate downstream analysis, ensuring seamless integration with many R packages. The workflow is easily implemented by cloning the GitHub repository, replacing the supplied input and reference files and editing a configuration file. Although we have selected the tools currently included in ARMOR, the setup is modular and alternative tools can be easily integrated.