RNA-Seq (named as an abbreviation of RNA sequencing) is a technique that uses next-generation sequencing to reveal the presence and quantity of RNA molecules in a biological sample, providing a snapshot of gene expression in the sample, also known as transcriptome.[2][3]
Specifically, RNA-Seq facilitates the ability to look at alternative gene spliced transcripts, post-transcriptional modifications, gene fusion, mutations/SNPs and changes in gene expression over time, or differences in gene expression in different groups or treatments.[4] 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.[5] RNA-Seq can also be used to determine exon/intron boundaries and verify or amend previously annotated5' and 3' gene boundaries. Recent advances in RNA-Seq include single cell sequencing, bulk RNA sequencing,[6] 3' mRNA-sequencing, in situ sequencing of fixed tissue, and native RNA molecule sequencing with single-molecule real-time sequencing.[7] Other examples of emerging RNA-Seq applications due to the advancement of bioinformatics algorithms are copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens.[8]
Prior to RNA-Seq, gene expression studies were done with hybridization-based microarrays. Issues with microarrays include cross-hybridization artifacts, poor quantification of lowly and highly expressed genes, and needing to know the sequence a priori.[9] Because of these technical issues, transcriptomics transitioned to sequencing-based methods. These progressed from Sanger sequencing of Expressed sequence tag libraries, to chemical tag-based methods (e.g., serial analysis of gene expression), and finally to the current technology, next-gen sequencing of complementary DNA (cDNA), notably RNA-Seq.
^Thind AS, Monga I, Thakur PK, Kumari P, Dindhoria K, Krzak M, et al. (November 2021). "Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology". Briefings in Bioinformatics. 22 (6). doi:10.1093/bib/bbab259. PMID34329375.