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dc.contributor.authorFischer, Carlos N. [UNESP]
dc.contributor.authorCampos, Victor De A. [UNESP]
dc.contributor.authorBarella, Victor H.
dc.date.accessioned2018-12-11T17:20:08Z
dc.date.available2018-12-11T17:20:08Z
dc.date.issued2018-05-01
dc.identifierhttp://dx.doi.org/10.1089/cmb.2017.0219
dc.identifier.citationJournal of Computational Biology, v. 25, n. 5, p. 517-527, 2018.
dc.identifier.issn1066-5277
dc.identifier.urihttp://hdl.handle.net/11449/176325
dc.description.abstractProfile hidden Markov models (pHMMs) have been used to search for transposable elements (TEs) in genomes. For the learning of pHMMs aimed to search for TEs of the retrotransposon class, the conventional protocol is to use the whole internal nucleotide portions of these elements as representative sequences. To further explore the potential of pHMMs in such a search, we propose five alternative ways to obtain the sets of representative sequences of TEs other than the conventional protocol. In this study, we are interested in Bel-PAO, Copia, Gypsy, and DIRS superfamilies from the retrotransposon class. We compared the pHMMs of all six protocols. The test results show that, for each TE superfamily, the pHMMs of at least two of the proposed protocols performed better than the conventional one and that the number of correct predictions provided by the latter can be improved by considering together the results of one or more of the alternative protocols.en
dc.format.extent517-527
dc.language.isoeng
dc.relation.ispartofJournal of Computational Biology
dc.sourceScopus
dc.subjectprofile hidden Markov models
dc.subjectretrotransposons
dc.subjecttransposable elements.
dc.titleOn the Search for Retrotransposons: Alternative Protocols to Obtain Sequences to Learn Profile Hidden Markov Modelsen
dc.typeArtigo
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.description.affiliationDepartment of Statistics Applied Maths and Computer Sciences UNESP-São Paulo State University, Avenida 24-A, 1515 Rio Claro
dc.description.affiliationDepartment of Computer Sciences Institute of Biosciences UNESP-São Paulo State University
dc.description.affiliationDepartment of Computer Sciences Institute of Mathematical and Computer Sciences USP - University of São Paulo
dc.description.affiliationUnespDepartment of Statistics Applied Maths and Computer Sciences UNESP-São Paulo State University, Avenida 24-A, 1515 Rio Claro
dc.description.affiliationUnespDepartment of Computer Sciences Institute of Biosciences UNESP-São Paulo State University
dc.identifier.doi10.1089/cmb.2017.0219
dc.rights.accessRightsAcesso aberto
dc.identifier.scopus2-s2.0-85046884932
unesp.author.lattes1858554355077119[1]
unesp.author.orcid0000-0002-5598-6263[1]
dc.relation.ispartofsjr0,824
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