The bench scientist's guide to statistical analysis of RNA-Seq data.

ABSTRACT:

Background

RNA sequencing (RNA-Seq) is emerging as a highly accurate method to quantify transcript abundance. However, analyses of the large data sets obtained by sequencing the entire transcriptome of organisms have generally been performed by bioinformatics specialists. Here we provide a step-by-step guide and outline a strategy using currently available statistical tools that results in a conservative list of differentially expressed genes. We also discuss potential sources of error in RNA-Seq analysis that could alter interpretation of global changes in gene expression.

Findings

When comparing statistical tools, the negative binomial distribution-based methods, edgeR and DESeq, respectively identified 11,995 and 11,317 differentially expressed genes from an RNA-seq datas

SUBMITTER: Yendrek CR 

PROVIDER: S-EPMC3522531 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature