As a result, if two genes transcribed from opposite strands overlap, nonstranded RNA‐seq cannot tell the true origin if a read falls into overlapping regions. In complex mammalian genomes, both DNA strands encode genes. Overview of mRNA‐seq laboratory flowchart and data analysis pipeline.

Despite the fact that a large number of algorithms have been developed for RNA‐seq data analysis in recent years, there are still many open questions for accurate read mapping, gene quantification, and data normalization. For most RNA‐seq studies, the data analyses consist of the following key steps : (1) quality check and preprocessing of raw sequence reads, (2) mapping reads to a reference genome or transcriptome, (3) counting reads mapped to individual genes or transcripts, (4) identification of differential expression (DE) genes between different biological conditions, and (5) biological interpretation of DE genes and functional enrichment analysis. After size selection, millions or even billions of short sequence reads are generated from a randomly fragmented cDNA library.

Polyadenylated (Poly‐A) RNA transcripts (for the so‐called mRNA‐seq) are enriched with oligo (dT) primers and then fragmented. The overview of current RNA‐seq approaches using shotgun sequencing technologies such as Illumina and the corresponding data analysis workflow is summarized in Figure 1. Compared with microarrays, it not only avoids some of the technical limitations of this approach including varying probe performance and nonspecific hybridization, and dynamic range issues, but can also detect alternative splicing isoforms and subtle changes of splicing under different conditions. In recent years, RNA sequencing (RNA‐seq) has emerged as a powerful technology for transcriptome profiling. In the meantime, RNA‐seq is evolving rapidly, and newer sequencing technologies are briefly introduced, including stranded RNA‐seq, targeted RNA‐seq, and single‐cell RNA‐seq. In this chapter, we review the main tools and algorithms currently available for RNA‐seq data analyses, and our goal is to help RNA‐seq data analysts to make an informed choice of tools in practical RNA‐seq data analysis. Despite the fact that multiple algorithms pertinent to basic analyses have been developed, there are still a variety of unresolved questions. RNA‐seq data analyses typically consist of (1) accurate mapping of millions of short sequencing reads to a reference genome, including the identification of splicing events (2) quantifying expression levels of genes, transcripts, and exons (3) differential analysis of gene expression among different biological conditions and (4) biological interpretation of differentially expressed genes. While RNA sequencing (RNA‐seq) has become increasingly popular for transcriptome profiling, the analysis of the massive amount of data generated by large‐scale RNA‐seq still remains a challenge.

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