期刊
G3-GENES GENOMES GENETICS
卷 9, 期 7, 页码 2089-2096出版社
OXFORD UNIV PRESS INC
DOI: 10.1534/g3.119.400185
关键词
RNA sequencing; Differential expression; Exploratory data analysis; Quality control
资金
- University Research Priority Program Evolution in Action at the University of Zurich
- Swiss National Science Foundation [310030_175841, CRSII5_177208]
- Chan Zuckerberg Initiative [2018-182828]
- Swiss National Science Foundation (SNF) [310030_175841, CRSII5_177208] Funding Source: Swiss National Science Foundation (SNF)
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.
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