Literature DB >> 31088905

ARMOR: An Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq Data.

Stephany Orjuela1,2,3, Ruizhu Huang1,2, Katharina M Hembach1,2,4, Mark D Robinson5,2, Charlotte Soneson5,2.   

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.
Copyright © 2019 Orjuela et al.

Entities:  

Keywords:  Differential expression; Exploratory data analysis; Quality control; RNA sequencing

Mesh:

Year:  2019        PMID: 31088905      PMCID: PMC6643886          DOI: 10.1534/g3.119.400185

Source DB:  PubMed          Journal:  G3 (Bethesda)        ISSN: 2160-1836            Impact factor:   3.154


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