Format

Send to

Choose Destination
Nat Protoc. 2013 Sep;8(9):1765-86. doi: 10.1038/nprot.2013.099. Epub 2013 Aug 22.

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

Author information

1
Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.

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.

PMID:
23975260
DOI:
10.1038/nprot.2013.099
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Nature Publishing Group
Loading ...
Support Center