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microTSS: Accurate microRNA transcription start site identification reveals a significant number of divergent pri-miRNAs

dc.creatorGeorgakilas, G.en
dc.creatorVlachos, I. S.en
dc.creatorParaskevopoulou, M. D.en
dc.creatorYang, P.en
dc.creatorZhang, Y.en
dc.creatorEconomides, A. N.en
dc.creatorHatzigeorgiou, A. G.en
dc.date.accessioned2015-11-23T10:27:31Z
dc.date.available2015-11-23T10:27:31Z
dc.date.issued2014
dc.identifier10.1038/ncomms6700
dc.identifier.issn20411723
dc.identifier.urihttp://hdl.handle.net/11615/27727
dc.description.abstractA large fraction of microRNAs (miRNAs) are derived from intergenic non-coding loci and the identification of their promoters remains 'elusive'. Here, we present microTSS, a machine-learning algorithm that provides highly accurate, single-nucleotide resolution predictions for intergenic miRNA transcription start sites (TSSs). MicroTSS integrates high-resolution RNA-sequencing data with active transcription marks derived from chromatin immunoprecipitation and DNase-sequencing to enable the characterization of tissue-specific promoters. MicroTSS is validated with a specifically designed Drosha-null/conditional-null mouse model, generated using the conditional by inversion (COIN) methodology. Analyses of global run-on sequencing data revealed numerous pri-miRNAs in human and mouse either originating from divergent transcription at promoters of active genes or partially overlapping with annotated long non-coding RNAs. MicroTSS is readily applicable to any cell or tissue samples and constitutes the missing part towards integrating the regulation of miRNA transcription into the modelling of tissue-specific regulatory networks. © 2014 Macmillan Publishers Limited. All rights reserved.en
dc.source.urihttp://www.scopus.com/inward/record.url?eid=2-s2.0-84923263791&partnerID=40&md5=a5ff43d1f2aade28e66e6e5a8e0d425b
dc.subjectcomplementary RNAen
dc.subjectdeoxyribonucleaseen
dc.subjectdivergent pri microRNAen
dc.subjectlong untranslated RNAen
dc.subjectmicroRNAen
dc.subjectRNA precursoren
dc.subjectsmall nucleolar RNAen
dc.subjectunclassified drugen
dc.subjectantisense oligonucleotideen
dc.subjectDrosha protein, mouseen
dc.subjectmessenger RNAen
dc.subjectribonuclease IIIen
dc.subjectRNA polymerase IIen
dc.subjectuntranslated RNAen
dc.subjectaccuracy assessmenten
dc.subjectalgorithmen
dc.subjectdata acquisitionen
dc.subjectenzyme activityen
dc.subjectproteinen
dc.subjectRNAen
dc.subjectanimal cellen
dc.subjectArticleen
dc.subjectchromatin immunoprecipitationen
dc.subjectconditional by inversion methodologyen
dc.subjectcontrolled studyen
dc.subjectDNA sequenceen
dc.subjectdown regulationen
dc.subjectembryonic stem cellen
dc.subjectenhancer regionen
dc.subjectgene clusteren
dc.subjecthumanen
dc.subjecthuman cellen
dc.subjectintronen
dc.subjectmethodologyen
dc.subjectmouseen
dc.subjectnonhumanen
dc.subjectnull alleleen
dc.subjectpromoter regionen
dc.subjecttranscription initiation siteen
dc.subjectwild typeen
dc.subjectanimalen
dc.subjectbiological modelen
dc.subjectbiologyen
dc.subjectcluster analysisen
dc.subjectcytologyen
dc.subjectgeneticsen
dc.subjectmetabolismen
dc.subjectsequence analysisen
dc.subjectsupport vector machineen
dc.subjecttransgenic mouseen
dc.subjectAlgorithmsen
dc.subjectAnimalsen
dc.subjectComputational Biologyen
dc.subjectEmbryonic Stem Cellsen
dc.subjectHumansen
dc.subjectMiceen
dc.subjectMice, Transgenicen
dc.subjectMicroRNAsen
dc.subjectModels, Geneticen
dc.subjectOligonucleotides, Antisenseen
dc.subjectPromoter Regions, Geneticen
dc.subjectRNA, Messengeren
dc.subjectRNA, Untranslateden
dc.subjectSequence Analysis, RNAen
dc.subjectSupport Vector Machinesen
dc.titlemicroTSS: Accurate microRNA transcription start site identification reveals a significant number of divergent pri-miRNAsen
dc.typejournalArticleen


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