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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-09

AUTHORS

Simon Anders, Davis J McCarthy, Yunshun Chen, Michal Okoniewski, Gordon K Smyth, Wolfgang Huber, Mark D Robinson

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. More... »

PAGES

1765-1786

References to SciGraph publications

  • 2010-03. A scaling normalization method for differential expression analysis of RNA-seq data in GENOME BIOLOGY
  • 2010-10. Differential expression analysis for sequence count data in GENOME BIOLOGY
  • 2013-12. A comparison of methods for differential expression analysis of RNA-seq data in BMC BIOINFORMATICS
  • 2004-09. Bioconductor: open software development for computational biology and bioinformatics in GENOME BIOLOGY
  • 2013-01. Epigenetic expansion of VHL-HIF signal output drives multiorgan metastasis in renal cancer in NATURE MEDICINE
  • 2009-05. How to map billions of short reads onto genomes in NATURE BIOTECHNOLOGY
  • 2011-12. GC-Content Normalization for RNA-Seq Data in BMC BIOINFORMATICS
  • 2002. Sweave: Dynamic Generation of Statistical Reports Using Literate Data Analysis in COMPSTAT
  • 2008-07. Mapping and quantifying mammalian transcriptomes by RNA-Seq in NATURE METHODS
  • 2013-06. Rev-Erbs repress macrophage gene expression by inhibiting enhancer-directed transcription in NATURE
  • 2010-05. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation in NATURE BIOTECHNOLOGY
  • 2010-12. baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data in BMC BIOINFORMATICS
  • 2010-10. Tackling the widespread and critical impact of batch effects in high-throughput data in NATURE REVIEWS GENETICS
  • 2009-01. RNA-Seq: a revolutionary tool for transcriptomics in NATURE REVIEWS GENETICS
  • 2012-01. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer in NATURE
  • 2010-12. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments in BMC BIOINFORMATICS
  • 2007-04. Unproductive splicing of SR genes associated with highly conserved and ultraconserved DNA elements in NATURE
  • 2011-07. Sequencing technology does not eliminate biological variability in NATURE BIOTECHNOLOGY
  • 2011-07. Full-length transcriptome assembly from RNA-Seq data without a reference genome in NATURE BIOTECHNOLOGY
  • 2012-03-01. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks in NATURE PROTOCOLS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/nprot.2013.099

    DOI

    http://dx.doi.org/10.1038/nprot.2013.099

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1033430059

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/23975260


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