RNA-Seq is a powerful technology that aims to uncover the existence or absence of RNA at any given time in the genome. The transcriptome as we call it is very dynamic and is constantly changing as opposed to a static genome. The recent developments of next-generation sequencing (NGS) allow for increased base coverage of a DNA sequence, as well as higher sample throughput.
This facilitates sequencing of the RNA transcripts in a cell, providing the ability to look at alternative gene spliced transcripts, post-transcriptional changes, gene fusion, mutations/SNPs, and changes in gene expression. In addition to mRNA transcripts, RNA-Seq can look at different populations of RNA to include total RNA, small RNA such as miRNA, tRNA, and ribosomal profiling. RNA-Seq can also be used to determine exon/intron boundaries and verify or amend previously annotated 5’ and 3’ gene boundaries.
Ongoing RNA-Seq research includes observing cellular pathway alterations during infection, and gene expression level changes in cancer studies. Prior to NGS, transcriptomics and gene expression studies were previously done with expression microarrays, which contain thousands of DNA sequences that probe for a match in the target sequence, making available a profile of all transcripts being expressed.
This was later done with Serial Analysis of Gene Expression (SAGE). One deficiency with microarrays that makes RNA-Seq more attractive has been limited coverage; such arrays target the identification of known common alleles that represent approximately 500,000 to 2,000,000 SNPs of the more than 10,000,000 in the genome. As such, libraries aren’t usually available to detect and evaluate rare allele variant transcripts, and the arrays are only as good as the SNP databases they’re designed from, so they have limited application for research purposes. Many cancers for example are caused by rare <1% mutations and would go undetected.
We have a lot of collective experience in this field of genetic research in the form of geneticists, statisticians, and computational biologists. We provide cutting-edge sequencing, data analysis, and support to numerous researchers in UT Southwestern and beyond. The following is a basic workflow that we employ for the analysis of such data.
Please contact us f you would like more details about the workflow including specific parameters of the software, genome versions used, etc. This publication is also a great place to start for those who want a basic introduction as well as analysis procedures for RNA-Seq data.