Human microRNA prediction through a probabilistic co-learning model of sequence and structure

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dc.contributor.authorNam, JWko
dc.contributor.authorShin, KRko
dc.contributor.authorHan, JJko
dc.contributor.authorLee, Yko
dc.contributor.authorKim, VNko
dc.contributor.authorZhang, BTko
dc.date.accessioned2017-03-28T06:59:24Z-
dc.date.available2017-03-28T06:59:24Z-
dc.date.created2017-03-08-
dc.date.created2017-03-08-
dc.date.issued2005-
dc.identifier.citationNUCLEIC ACIDS RESEARCH, v.33, no.11, pp.3570 - 3581-
dc.identifier.issn0305-1048-
dc.identifier.urihttp://hdl.handle.net/10203/221053-
dc.description.abstractMicroRNAs (miRNAs) are small regulatory RNAs of similar to 22 nt. Although hundreds of miRNAs have been identified through experimental complementary DNA cloning methods and computational efforts, previous approaches could detect only abundantly expressed miRNAs or close homologs of previously identified miRNAs. Here, we introduce a probabilistic co-learning model for miRNA gene finding, ProMiR, which simultaneously considers the structure and sequence of miRNA precursors (pre-miRNAs). On 5-fold cross-validation with 136 referenced human datasets, the efficiency of the classification shows 73% sensitivity and 96% specificity. When applied to genome screening for novel miRNAs on human chromosomes 16, 17, 18 and 19, ProMiR effectively searches distantly homologous patterns over diverse pre-miRNAs, detecting at least 23 novel miRNA gene candidates. Importantly, the miRNA gene candidates do not demonstrate clear sequence similarity to the known miRNA genes. By quantitative PCR followed by RNA interference against Drosha, we experimentally confirmed that 9 of the 23 representative candidate genes express transcripts that are processed by the miRNA biogenesis enzyme Drosha in HeLa cells, indicating that ProMiR may successfully predict miRNA genes with at least 40% accuracy. Our study suggests that the miRNA gene family may be more abundant than previously anticipated, and confer highly extensive regulatory networks on eukaryotic cells.-
dc.languageEnglish-
dc.publisherOXFORD UNIV PRESS-
dc.subjectRNA SECONDARY STRUCTURE-
dc.subjectNUCLEAR EXPORT-
dc.subjectCOMPUTATIONAL IDENTIFICATION-
dc.subjectNONCODING RNAS-
dc.subjectGENES-
dc.subjectINTERFERENCE-
dc.subjectPRECURSORS-
dc.subjectBIOGENESIS-
dc.subjectMOUSE-
dc.subjectMATURATION-
dc.titleHuman microRNA prediction through a probabilistic co-learning model of sequence and structure-
dc.typeArticle-
dc.identifier.wosid000230345800019-
dc.identifier.scopusid2-s2.0-21844477027-
dc.type.rimsART-
dc.citation.volume33-
dc.citation.issue11-
dc.citation.beginningpage3570-
dc.citation.endingpage3581-
dc.citation.publicationnameNUCLEIC ACIDS RESEARCH-
dc.identifier.doi10.1093/nar/gki668-
dc.contributor.localauthorHan, JJ-
dc.contributor.nonIdAuthorNam, JW-
dc.contributor.nonIdAuthorShin, KR-
dc.contributor.nonIdAuthorLee, Y-
dc.contributor.nonIdAuthorKim, VN-
dc.contributor.nonIdAuthorZhang, BT-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordPlusRNA SECONDARY STRUCTURE-
dc.subject.keywordPlusNUCLEAR EXPORT-
dc.subject.keywordPlusCOMPUTATIONAL IDENTIFICATION-
dc.subject.keywordPlusNONCODING RNAS-
dc.subject.keywordPlusGENES-
dc.subject.keywordPlusINTERFERENCE-
dc.subject.keywordPlusPRECURSORS-
dc.subject.keywordPlusBIOGENESIS-
dc.subject.keywordPlusMOUSE-
dc.subject.keywordPlusMATURATION-
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