RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping.
A survey of best practices for RNA-seq data analysis
RNA-seq workflow: gene-level exploratory analysis and DE
RNAseq analysis notes from Tommy Tang
RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR
An open RNA-Seq data analysis pipeline tutorial with an example
