Transcriptomics technologies: Difference between revisions

Revision as of 21:10, 21 September 2021

Revision as of 05:08, 3 November 2021

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=== Early attempts ===

The word "transcriptome" was first used in the 1990s.<ref name="#10022985">{{cite journal | vauthors = Piétu G, Mariage-Samson R, Fayein NA, Matingou C, Eveno E, Houlgatte R, Decraene C, Vandenbrouck Y, Tahi F, Devignes MD, Wirkner U, Ansorge W, Cox D, Nagase T, Nomura N, Auffray C | title = The Genexpress IMAGE knowledge base of the human brain transcriptome: a prototype integrated resource for functional and computational genomics | journal = Genome Research | volume = 9 | issue = 2 | pages = 195–209 | date = February 1999 | pmid = 10022985 | pmc = 310711 | doi=10.1101/gr.9.2.195| doi-broken-date = 31 MayOctober 2021 }}</ref><ref name="#9008165">{{cite journal | vauthors = Velculescu VE, Zhang L, Zhou W, Vogelstein J, Basrai MA, Bassett DE, Hieter P, Vogelstein B, Kinzler KW | title = Characterization of the yeast transcriptome | journal = Cell | volume = 88 | issue = 2 | pages = 243–51 | date = January 1997 | pmid = 9008165 | doi = 10.1016/S0092-8674(00)81845-0 | s2cid = 11430660 | doi-access = free }}</ref> In 1995, one of the earliest sequencing-based transcriptomic methods was developed, [[serial analysis of gene expression]] (SAGE), which worked by [[Sanger sequencing]] of concatenated random transcript fragments.<ref name="#7570003">{{cite journal | vauthors = Velculescu VE, Zhang L, Vogelstein B, Kinzler KW | title = Serial analysis of gene expression | journal = Science | volume = 270 | issue = 5235 | pages = 484–7 | date = October 1995 | pmid = 7570003 | doi = 10.1126/science.270.5235.484 | bibcode = 1995Sci...270..484V | s2cid = 16281846 }}</ref> Transcripts were quantified by matching the fragments to known genes. A variant of SAGE using high-throughput sequencing techniques, called digital gene expression analysis, was also briefly used.<ref name="#23290152" /><ref name="#9331369">{{cite journal | vauthors = Audic S, Claverie JM | title = The significance of digital gene expression profiles | journal = Genome Research | volume = 7 | issue = 10 | pages = 986–95 | date = October 1997 | pmid = 9331369 | doi = 10.1101/gr.7.10.986 | doi-access = free }}</ref> However, these methods were largely overtaken by high throughput sequencing of entire transcripts, which provided additional information on transcript structure such as [[alternative splicing|splice variants]].<ref name="#23290152" />

=== Development of contemporary techniques ===

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=== Diagnostics and disease profiling ===

Transcriptomic strategies have seen broad application across diverse areas of biomedical research, including disease [[diagnosis]] and [[Disease|profiling]].<ref name="#19015660" /><ref>{{Cite journal|last1=Tavassoly|first1=Iman|last2=Goldfarb|first2=Joseph|last3=Iyengar|first3=Ravi|date=2018-10-04|title=Systems biology primer: the basic methods and approaches|journal=Essays in Biochemistry|volume=62|issue=4|language=en|pages=487–500|doi=10.1042/EBC20180003|issn=0071-1365|pmid=30287586|s2cid=52922135}}</ref> RNA-Seq approaches have allowed for the large-scale identification of [[transcriptional start sites]], uncovered alternative [[Promoter (genetics)|promoter]] usage, and novel [[Alternative splicing|splicing alterations]]. These [[Regulatory sequence|regulatory elements]] are important in human disease and, therefore, defining such variants is crucial to the interpretation of [[Genome-wide association study|disease-association studies]].<ref name="#22739340">{{cite journal | vauthors = Costa V, Aprile M, Esposito R, Ciccodicola A | title = RNA-Seq and human complex diseases: recent accomplishments and future perspectives | journal = European Journal of Human Genetics | volume = 21 | issue = 2 | pages = 134–42 | date = February 2013 | pmid = 22739340 | pmc = 3548270 | doi = 10.1038/ejhg.2012.129 }}</ref> RNA-Seq can also identify disease-associated [[single nucleotide polymorphism]]s (SNPs), allele-specific expression, and [[Fusion gene|gene fusions]], which contributes to the understanding of disease causal variants.<ref name="#26781813">{{cite journal | vauthors = Khurana E, Fu Y, Chakravarty D, Demichelis F, Rubin MA, Gerstein M | title = Role of non-coding sequence variants in cancer | journal = Nature Reviews Genetics | volume = 17 | issue = 2 | pages = 93–108 | date = February 2016 | pmid = 26781813 | doi = 10.1038/nrg.2015.17 | s2cid = 14433306 }}</ref>

[[Retrotransposon]]s are [[transposable element]]s which proliferate within eukaryotic genomes through a process involving [[reverse transcription]]. RNA-Seq can provide information about the transcription of endogenous retrotransposons that may influence the transcription of neighboring genes by various [[Epigenetics#Mechanisms|epigenetic mechanisms]] that lead to disease.<ref name="#17363976">{{cite journal | vauthors = Slotkin RK, Martienssen R | title = Transposable elements and the epigenetic regulation of the genome | journal = Nature Reviews Genetics | volume = 8 | issue = 4 | pages = 272–85 | date = April 2007 | pmid = 17363976 | doi = 10.1038/nrg2072 | s2cid = 9719784 }}</ref> Similarly, the potential for using RNA-Seq to understand [[Immune disorder|immune-related disease]] is expanding rapidly due to the ability to dissect immune cell populations and to sequence [[T cell receptor|T cell]] and [[B-cell receptor|B cell receptor]] repertoires from patients.<ref name="#26551575">{{cite journal | vauthors = Proserpio V, Mahata B | title = Single-cell technologies to study the immune system | journal = Immunology | volume = 147 | issue = 2 | pages = 133–40 | date = February 2016 | pmid = 26551575 | pmc = 4717243 | doi = 10.1111/imm.12553 }}</ref><ref name="#26996076">{{cite journal | vauthors = Byron SA, Van Keuren-Jensen KR, Engelthaler DM, Carpten JD, Craig DW | title = Translating RNA sequencing into clinical diagnostics: opportunities and challenges | journal = Nature Reviews Genetics | volume = 17 | issue = 5 | pages = 257–71 | date = May 2016 | pmid = 26996076 | doi = 10.1038/nrg.2016.10 | pmc = 7097555 }}</ref>