Differential expression for RNA sequencing (RNA-Seq) data: Mapping, summarization, statistical analysis, and experimental design

Abstract

RNA sequencing (RNA-seq) is an exciting technique that gives experimenters unprecedented access to information on transcriptome complexity. The costs are decreasing, data analysis methods are maturing, and the flexibility that RNA-seq affords will allow it to become the platform of choice for gene expression analysis. Here, we focus on differential expression (DE) analysis using RNA-seq, highlighting aspects of mapping reads to a reference transcriptome, quantification of expression levels, normalization for composition biases, statistical modeling to account for biological variability and experimental design considerations. We also comment on recent developments beyond the analysis of DE us..