RNA-Seq refers to the combination of a high-throughput sequencing methodology with computational methods to capture and quantify transcripts present in an RNA extract. The nucleotide sequences generated are typically around 100 bp in length, but can range from 30 bp to over 10,000 bp depending on the sequencing method used. RNA-Seq leverages deep sampling of the transcriptome with many short fragments from a transcriptome to allow computational reconstruction of the original RNA transcript by aligning reads to a reference genome or to each other (de novo assembly). Both low-abundance and high-abundance RNAs can be quantified in an RNA-Seq experiment (dynamic range of 5 orders of magnitude)—a key advantage over microarray transcriptomes. In addition, input RNA amounts are much lower for RNA-Seq (nanogram quantity) compared to microarrays (microgram quantity), which allow examination of the transcriptome even at a single-cell resolution when combined with amplification of cDNA. Theoretically, there is no upper limit of quantification in RNA-Seq, and background noise is very low for 100 bp reads in non-repetitive regions.
RNA-Seq may be used to identify genes within a genome, or identify which genes are active at a particular point in time, and read counts can be used to accurately model the relative gene expression level. RNA-Seq methodology has constantly improved, primarily through the development of DNA sequencing technologies to increase throughput, accuracy, and read length. Since the first descriptions in 2006 and 2008, RNA-Seq has been rapidly adopted and overtook microarrays as the dominant transcriptomics technique in 2015.
The quest for transcriptome data at the level of individual cells has driven advances in RNA-Seq library preparation methods, resulting in dramatic advances in sensitivity. Single-cell transcriptomes are now well described and have even been extended to in situ RNA-Seq where transcriptomes of individual cells are directly interrogated in fixed tissues.