The technologies originally developed for next-generation DNA sequencing can also be applied to deep sequencing of the transcriptome. This is done through cDNA sequencing and is called RNA sequencing or simply RNA-seq.
One of the key advantages of RNA-seq is that the method is independent of prior knowledge of the corresponding genomic sequences and therefore can be used to identify transcripts from unannotated genes, novel splicing isoforms, and gene-fusion transcripts [Wang et al., 2009,Martin and Wang, 2011]. Another strength is that it opens up for studies of transcriptomic complexities such as deciphering allele-specific transcription by the use of SNPs present in the transcribed regions [Heap et al., 2010].
RNA-seq-based transcriptomic studies have the potential to increase the overall understanding of the transcriptome. However, the key to get access to the hidden information and be able to make a meaningful interpretation of the sequencing data highly relies on the downstream bioinformatic analysis.
In this chapter we will first discuss the initial steps in the data analysis that lie upstream of the analysis using ready-to-use workflows. Next, we will look at what the individual ready-to-use workflows can be used for and go through step by step how to run the workflows.
- Automatic analysis of RNA-seq data
- Analysis of multiple samples
- Annotate Variants (WTS)
- Compare variants in DNA and RNA
- Identify Candidate Variants and Genes from Tumor Normal Pair
- Identify variants and add expression values
- Identify and Annotate Differentially Expressed Genes and Pathways