Quality Control and Analysis of NGS RNA Sequencing Data

Transcriptome sequencing, where RNA is isolated, converted to library of cDNA fragments, and sequenced using next-generation sequencing technology, has become the method of choice for the genome-wide characterization of mRNA levels. It offers a more accurate quantification of transcript levels than array-based methods, but also has the added benefit of allowing the discovery of novel gene/transcripts, alternative splice junctions, and novel RNAs. In addition, RNA sequencing may be used to investigate differential gene expression, allelic imbalance, eQTL mapping, RNA editing, RNA-protein interactions, and alternative splicing. A number of statistical methods and tools are available for differential expression analysis using RNA sequencing data and these are continually being developed and improved to handle more complex experimental designs. This chapter describes an example workflow for the quality control and analysis of raw RNA sequencing reads for the purposes of differential gene expression analysis, followed by pathway/enrichment analysis of significantly different genes. The methods and tools described are just one example of how this analysis can be conducted, but they can be applied to most standard RNA sequencing studies of differential gene expression. The methods covered are based on Illumina HiSeq single-end 50 bp reads. However, all programs used are capable of working with paired-end data, subsequent to minor adaptations.