Combining results from distinct microRNA target prediction tools enhances the performance of analyses

dc.contributor.authorOliveira, Arthur C. [UNESP]
dc.contributor.authorBovolenta, Luiz A. [UNESP]
dc.contributor.authorNachtigall, Pedro G. [UNESP]
dc.contributor.authorHerkenhoff, Marcos E. [UNESP]
dc.contributor.authorLemke, Ney [UNESP]
dc.contributor.authorPinhal, Danillo [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-12-11T17:12:20Z
dc.date.available2018-12-11T17:12:20Z
dc.date.issued2017-05-09
dc.description.abstractTarget prediction is generally the first step toward recognition of bona fide microRNA (miRNA)-target interactions in living cells. Several target prediction tools are now available, which use distinct criteria and stringency to provide the best set of candidate targets for a single miRNA or a subset of miRNAs. However, there are many false-negative predictions, and consensus about the optimum strategy to select and use the output information provided by the target prediction tools is lacking. We compared the performance of four tools cited in literature-TargetScan (TS), miRanda-mirSVR (MR), Pita, and RNA22 (R22), and we determined the most effective approach for analyzing target prediction data (individual, union, or intersection). For this purpose, we calculated the sensitivity, specificity, precision, and correlation of these approaches using 10 miRNAs (miR-1-3p, miR-17-5p, miR-21-5p, miR-24-3p, miR-29a-3p, miR-34a-5p, miR-124-3p, miR-125b-5p, miR-145-5p, and miR-155-5p) and 1,400 genes (700 validated and 700 non-validated) as targets of these miRNAs. The four tools provided a subset of high-quality predictions and returned few false-positive predictions; however, they could not identify several known true targets. We demonstrate that union of TS/MR and TS/MR/R22 enhanced the quality of in silico prediction analysis of miRNA targets. We conclude that the union rather than the intersection of the aforementioned tools is the best strategy for maximizing performance while minimizing the loss of time and resources in subsequent in vivo and in vitro experiments for functional validation of miRNA-target interactions.en
dc.description.affiliationLaboratory of Genomics and Molecular Evolution Department of Genetics Institute of Biosciences of Botucatu São Paulo State Univesity (UNESP)
dc.description.affiliationLaboratory of Bioinformatics and Computational Biophysics Department of Physics and Biophysics Institute of Biosciences of Botucatu São Paulo State Univesity (UNESP)
dc.description.affiliationUnespLaboratory of Genomics and Molecular Evolution Department of Genetics Institute of Biosciences of Botucatu São Paulo State Univesity (UNESP)
dc.description.affiliationUnespLaboratory of Bioinformatics and Computational Biophysics Department of Physics and Biophysics Institute of Biosciences of Botucatu São Paulo State Univesity (UNESP)
dc.identifierhttp://dx.doi.org/10.3389/fgene.2017.00059
dc.identifier.citationFrontiers in Genetics, v. 8, n. MAY, 2017.
dc.identifier.doi10.3389/fgene.2017.00059
dc.identifier.file2-s2.0-85019936207.pdf
dc.identifier.issn1664-8021
dc.identifier.lattes7977035910952141
dc.identifier.scopus2-s2.0-85019936207
dc.identifier.urihttp://hdl.handle.net/11449/174667
dc.language.isoeng
dc.relation.ispartofFrontiers in Genetics
dc.relation.ispartofsjr2,274
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectBioinformatics
dc.subjectIn silico prediction
dc.subjectMiRanda-mirSVR
dc.subjectNon-coding RNA
dc.subjectPita
dc.subjectRNA22
dc.subjectTargetScan
dc.titleCombining results from distinct microRNA target prediction tools enhances the performance of analysesen
dc.typeArtigo
unesp.advisor.lattes7977035910952141

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