44 Current Challenges in Mirnomics

dc.contributor.author Akgül, Bünyamin
dc.contributor.author Stadler, Peter F.
dc.contributor.author Hawkins, Liam J.
dc.contributor.author Hadj-Moussa, Hanane
dc.contributor.author Storey, Kenneth B.
dc.contributor.author Ergin, Kemal
dc.contributor.author Allmer, Jens
dc.date.accessioned 2021-11-06T09:23:36Z
dc.date.available 2021-11-06T09:23:36Z
dc.date.issued 2022
dc.description.abstract Mature microRNAs (miRNAs) are short RNA sequences about 18–24 nucleotide long, which provide the recognition key within RISC for the posttranscriptional regulation of target RNAs. Considering the canonical pathway, mature miRNAs are produced via a multistep process. Their transcription (pri-miRNAs) and first processing step via the microprocessor complex (pre-miRNAs) occur in the nucleus. Then they are exported into the cytosol, processed again by Dicer (dsRNA) and finally a single strand (mature miRNA) is incorporated into RISC (miRISC). The sequence of the incorporated miRNA provides the function of RNA target recognition via hybridization. Following binding of the target, the mRNA is either degraded or translation is inhibited, which ultimately leads to less protein production. Conversely, it has been shown that binding within the 5? UTR of the mRNA can lead to an increase in protein product. Regulation of homeostasis is very important for a cell; therefore, all steps in the miRNA-based regulation pathway, from transcription to the incorporation of the mature miRNA into RISC, are under tight control. While much research effort has been exerted in this area, the knowledgebase is not sufficient for accurately modelling miRNA regulation computationally. The computational prediction of miRNAs is, however, necessary because it is not feasible to investigate all possible pairs of a miRNA and its target, let alone miRNAs and their targets. We here point out open challenges important for computational modelling or for our general understanding of miRNA-based regulation and show how their investigation is beneficial. It is our hope that this collection of challenges will lead to their resolution in the near future. © 2022, Springer Science+Business Media, LLC, part of Springer Nature. en_US
dc.identifier.doi 10.1007/978-1-0716-1170-8_19
dc.identifier.issn 1064-3745
dc.identifier.scopus 2-s2.0-85114096543
dc.identifier.uri http://doi.org/10.1007/978-1-0716-1170-8_19
dc.identifier.uri https://hdl.handle.net/11147/11243
dc.language.iso en en_US
dc.publisher Humana Press en_US
dc.relation.ispartof Methods in Molecular Biology en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Challenges en_US
dc.subject Mature miRNA en_US
dc.subject miRNA prediction en_US
dc.subject miRNA targeting en_US
dc.subject miRNomics en_US
dc.subject RISC en_US
dc.title 44 Current Challenges in Mirnomics en_US
dc.type Book Part en_US
dspace.entity.type Publication
gdc.author.institutional Akgül, Bünyamin
gdc.bip.impulseclass C4
gdc.bip.influenceclass C5
gdc.bip.popularityclass C4
gdc.coar.access metadata only access
gdc.coar.type text::book::book part
gdc.collaboration.industrial false
gdc.description.department İzmir Institute of Technology. Molecular Biology and Genetics en_US
gdc.description.endpage 438 en_US
gdc.description.publicationcategory Kitap Bölümü - Uluslararası en_US
gdc.description.scopusquality Q4
gdc.description.startpage 423 en_US
gdc.description.volume 2257 en_US
gdc.description.wosquality N/A
gdc.identifier.openalex W3193812814
gdc.identifier.pmid 34432289
gdc.index.type Scopus
gdc.index.type PubMed
gdc.oaire.diamondjournal false
gdc.oaire.impulse 7.0
gdc.oaire.influence 2.8462597E-9
gdc.oaire.isgreen false
gdc.oaire.keywords MicroRNAs
gdc.oaire.keywords Gene Expression Regulation
gdc.oaire.keywords Genomics
gdc.oaire.keywords RNA, Messenger
gdc.oaire.popularity 8.478941E-9
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 0301 basic medicine
gdc.oaire.sciencefields 0303 health sciences
gdc.oaire.sciencefields 03 medical and health sciences
gdc.openalex.collaboration International
gdc.openalex.fwci 10.36350151
gdc.openalex.normalizedpercentile 0.95
gdc.openalex.toppercent TOP 10%
gdc.opencitations.count 7
gdc.plumx.crossrefcites 4
gdc.plumx.mendeley 5
gdc.plumx.pubmedcites 4
gdc.plumx.scopuscites 4
gdc.scopus.citedcount 4
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relation.isOrgUnitOfPublication.latestForDiscovery 9af2b05f-28ac-4013-8abe-a4dfe192da5e

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