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Nature Research, Nature Protocols, 9(8), p. 1765-1786, 2013

DOI: 10.1038/nprot.2013.099

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Count-based differential expression analysis of RNA sequencing data using R and Bioconductor

Journal article published in 2013 by Simon Anders ORCID, Yunshun Chen, Davis J. McCarthy, Michal Okoniewski, Gordon K. Smyth ORCID, Wolfgang Huber ORCID, Mark D. Robinson ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

RNA sequencing (RNA-seq) has been rapidly adopted for the profiling of transcriptomes in many areas of biology, including studies into gene regulation, development and disease. Of particular interest is the discovery of differentially expressed genes across different conditions (e.g., tissues, perturbations) while optionally adjusting for other systematic factors that affect the data-collection process. There are a number of subtle yet crucial aspects of these analyses, such as read counting, appropriate treatment of biological variability, quality control checks and appropriate setup of statistical modeling. Several variations have been presented in the literature, and there is a need for guidance on current best practices. This protocol presents a state-of-the-art computational and statistical RNA-seq differential expression analysis workflow largely based on the free open-source R language and Bioconductor software and, in particular, on two widely used tools, DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4-10 samples) can be <1 h, with computation time <1 d using a standard desktop PC.