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

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 critical 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, two widely-used tools DESeq and edgeR. Hands-on time for typical small experiments (e. g., 4-10 samples) can be <1 hour, with computation time <1 day using a standard desktop PC.

Characterization of FN1–FGFR1 and novel FN1–FGF1 fusion genes in a large series of phosphaturic mesenchymal tumors

“…8 Multidimensional scaling plot and principal component analysis plot were generated with EdgeR and DESeq software, respectively, to determine the differential global expression between samples. 12 Validation of Fusion Genes and Proteins with PCR, Direct Sequencing, and Western Blot…”

Genomewide transcriptional signatures of migratory flight activity in a globally invasive insect pest

“…1 using a union of two separate methods as implemented in edgeR and DESeq2 (Anders et al . 2013). Any gene significantly expressed in either the China or Greece experiment was considered a candidate gene for flight.…”

“…2013). Both packages use the negative binomial model for analysing RNA‐seq count data but differ in their estimation of gene dispersal.…”

Beyond gene expression level: How are Bayesian methods doing a great job in quantification of isoform diversity and allelic imbalance?

“…High-throughput gene expression profile data in both array and sequencing have played a key role to address fundamental questions to arise in biomedical research (Anders and Huber, 2010;Anders et al, 2013;Aryee et al, 2009;Bar-Joseph et al, 2012;Bi and Davuluri, 2013;Bullard et al, 2010;Cumbie et al, 2011;Gao and Song, 2005;Hardcastle and Kelly, 2010;Hu et al, 2014;Jiang and Wong, 2009;Lee et al, 2011;Li and Jiang, 2012;Lin et al, 2003;Ma and Zhang, 2013;Marioni et al, 2008;Nariai et al, 2014;Nishiu et al, 2002;Oh et al, 2013;Oshlack et al, 2010;Pollier et al, 2013;Rehrauer et al, 2013;Roberts et al, 2011;Robinson and Oshlack, 2010;Shi and Jiang, 2013;Skelly et al, 226 Sunghee Oh · Chul Soo Kim 2011; Stegle et al, 2010;Suo et al, 2014;Tarazona et al, 2011;Trapnell et al, 2012;Vardhanabhuti et al, 2013;Wang et al, 2013;Wang et al, 2010;Wu et al, 2011b;Zhao et al, 2008). Simultaneous statistical testing based on millions of transcripts and corresponding mRNA samples has made it possible to identify biomarkers that are crucially influencing the alteration of expression levels between disease and normal samples/conditions, classification of sub-groups of particular disease, therapeutic effects on biological external condition such as drug treatments and disease progression over a series of diffe...…”

“…Simultaneous statistical testing based on millions of transcripts and corresponding mRNA samples has made it possible to identify biomarkers that are crucially influencing the alteration of expression levels between disease and normal samples/conditions, classification of sub-groups of particular disease, therapeutic effects on biological external condition such as drug treatments and disease progression over a series of different stages, etc (Anders and Huber, 2010;Anders et al, 2013;Bi and Davuluri, 2013;Bullard et al, 2010;Hardcastle and Kelly, 2010;Lin et al, 2003;Oshlack et al, 2010;Rehrauer et al, 2013;Robinson and Oshlack, 2010;Tarazona et al, 2011). Ultimately, exploring the complexity of disease progressive mechanisms in large-scale of expression profiles cover the entire protocol in the context of biologically hypothetical questionnaire, experimental design, analytical pipeline and corresponding statistical and computational strategy, and validated results with interpretation and take-home messages.…”