Learning HMMs for nucleotide sequences from amino acid alignments

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dc.contributor.authorFischer, Carlos Norberto [UNESP]
dc.contributor.authorCarareto, Claudia Marcia [UNESP]
dc.contributor.authorSantos, Renato Augusto Corrêa dos [UNESP]
dc.contributor.authorCerri, Ricardo
dc.contributor.authorCosta, Eduardo
dc.contributor.authorSchietgat, Leander
dc.contributor.authorVens, Celine
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionKatholieke Universiteit Leuven
dc.date.accessioned2015-10-21T20:14:38Z
dc.date.available2015-10-21T20:14:38Z
dc.date.issued2015-06-01
dc.description.abstractProfile hidden Markov models (profile HMMs) are known to efficiently predict whether an amino acid (AA) sequence belongs to a specific protein family. Profile HMMs can also be used to search for protein domains in genome sequences. In this case, HMMs are typically learned from AA sequences and then used to search on the six-frame translation of nucleotide (NT) sequences. However, this approach demands additional processing of the original data and search results. Here, we propose an alternative and more direct method which converts an AA alignment into an NT one, after which an NT-based HMM is trained to be applied directly on a genome.en
dc.description.affiliationUniversidade Federal de São Carlos, Departamento de Ciência da Computação
dc.description.affiliationUniversidade de São Paulo, Departamento de Ciência da Computação
dc.description.affiliationKatholieke Universiteit Leuven, Department of Computer Science
dc.description.affiliationKatholieke Universiteit Leuven, Department of Public Health and Primary Care
dc.description.affiliationUnespUniversidade Estadual Paulista, Departamento de Estatística, Matemática Aplicada e Computação, Instituto de Geociências e Ciências Exatas de Rio Claro
dc.description.affiliationUnespUniversidade Estadual Paulista, Departamento de Biologia, Instituto de Biociências de Rio Claro
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 2012/24774-2
dc.description.sponsorshipIdFAPESP: 2010/10731-4
dc.description.sponsorshipIdCNPq: 306493/2013-6
dc.format.extent1836-1838
dc.identifierhttp://bioinformatics.oxfordjournals.org/content/31/11/1836
dc.identifier.citationBioinformatics. Oxford: Oxford Univ Press, v. 31, n. 11, p. 1836-1838, 2015.
dc.identifier.doi10.1093/bioinformatics/btv054
dc.identifier.issn1367-4803
dc.identifier.lattes1858554355077119
dc.identifier.lattes3425772998319216
dc.identifier.orcid0000-0002-0298-1354
dc.identifier.urihttp://hdl.handle.net/11449/129033
dc.identifier.wosWOS:000356625300020
dc.language.isoeng
dc.publisherOxford Univ Press
dc.relation.ispartofBioinformatics
dc.relation.ispartofjcr5.481
dc.relation.ispartofsjr6,140
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.titleLearning HMMs for nucleotide sequences from amino acid alignmentsen
dc.typeArtigo
dcterms.licensehttp://www.oxfordjournals.org/access_purchase/self-archiving_policyb.html
dcterms.rightsHolderOxford Univ Press
unesp.author.lattes1858554355077119[1]
unesp.author.lattes3425772998319216[2]
unesp.author.orcid0000-0002-5598-6263[1]
unesp.author.orcid0000-0002-0298-1354[2]
unesp.campusUniversidade Estadual Paulista (Unesp), Instituto de Geociências e Ciências Exatas, Rio Claropt
unesp.campusUniversidade Estadual Paulista (Unesp), Instituto de Biociências, Rio Claropt

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Artigos - Estatistica, Matemática Aplicada e Computação - IGCE
Artigos - Biologia - IBRC