Transcriptomics technologies: Difference between revisions

Revision as of 23:53, 22 December 2023

Revision as of 13:59, 28 December 2023

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====Principles and advances====

[[RNA-Seq]] refers to the combination of a [[DNA sequencing#High-throughput methods|high-throughput sequencing]] methodology with computational methods to capture and quantify transcripts present in an RNA extract.<ref name="#19015660" /> 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 [[Coverage (genetics)|deep sampling]] of the transcriptome with many short fragments from a transcriptome to allow computational reconstruction of the original RNA transcript by [[Sequence alignment|aligning]] reads to a reference genome or to each other ([[De novo transcriptome assembly|de novo assembly]]).<ref name="#23290152" /> Both low-abundance and high-abundance RNAs can be quantified in an RNA-Seq experiment ([[dynamic range]] of 5 [[Order of magnitude|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 allows finerallow examination of cellularthe structurestranscriptome downeven toat thea single-cell levelresolution when combined with linear amplification of cDNA.<ref name="#22939981">{{cite journal | vauthors = Hashimshony T, Wagner F, Sher N, Yanai I | title = CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification | journal = Cell Reports | volume = 2 | issue = 3 | pages = 666–73 | date = September 2012 | pmid = 22939981 | doi = 10.1016/j.celrep.2012.08.003 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Svensson V, Vento-Tormo R, Teichmann SA | title = Exponential scaling of single-cell RNA-seq in the past decade | journal = Nature Protocols | volume = 13 | issue = 4 | pages = 599–604 | date = April 2018 | pmid = 29494575 | doi = 10.1038/nprot.2017.149 | s2cid = 3560001 }}</ref> 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.<ref name="#19015660" />

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.<ref>{{Cite journal|last=Tachibana|first=Chris|name-list-style = vanc |date=2015-08-18|title=Transcriptomics today: Microarrays, RNA-seq, and more|journal=Science|volume=349|issue=6247|page=544|doi=10.1126/science.opms.p1500095|bibcode=2015Sci...349..544T|doi-access=free}}</ref> Since the first descriptions in 2006 and 2008,<ref name="#17010196" /><ref name="#18451266">{{cite journal | vauthors = Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, Snyder M | title = The transcriptional landscape of the yeast genome defined by RNA sequencing | journal = Science | volume = 320 | issue = 5881 | pages = 1344–9 | date = June 2008 | pmid = 18451266 | pmc = 2951732 | doi = 10.1126/science.1158441 | bibcode = 2008Sci...320.1344N }}</ref> RNA-Seq has been rapidly adopted and overtook microarrays as the dominant transcriptomics technique in 2015.<ref name="#25633159">{{cite journal | vauthors = Su Z, Fang H, Hong H, Shi L, Zhang W, Zhang W, Zhang Y, Dong Z, Lancashire LJ, Bessarabova M, Yang X, Ning B, Gong B, Meehan J, Xu J, Ge W, Perkins R, Fischer M, Tong W | title = An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era | journal = Genome Biology | volume = 15 | issue = 12 | pages = 523 | date = December 2014 | pmid = 25633159 | pmc = 4290828 | doi = 10.1186/s13059-014-0523-y | doi-access = free }}</ref>