Whole transcriptome sequencing (WTS)
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.
The following template workflows are available for use with RNA-Seq data (figure 21.1):
- Differential Expression and Pathway Analysis
- Differential Expression and Pathway Analysis from Count Matrix
- For data from Human (
), Mouse and Rat (
):
- Annotate Variants (WTS)
- Compare Variants in DNA and RNA
- Identify Candidate Variants and Genes from Tumor Normal Pair
- Identify Variants and Add Expression Values
For the workflows where variants are annotated based on information from databases available for more than one population, you will have the opportunity to select the population that is best suited for your analysis.
Figure 21.1: The RNA-Seq template workflows are available under the Whole Transcriptome Sequencing folder.
Subsections
- Differential Expression and Pathway Analysis
- Annotate Variants (WTS)
- Compare Variants in DNA and RNA
- Identify Candidate Variants and Genes from Tumor Normal Pair
- Identify Variants and Add Expression Values