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An approach for COFFEE objective function to global DNA multiple sequence alignment

dc.contributor.authorAmorim, Anderson Rici [UNESP]
dc.contributor.authorNeves, Leandro Alves [UNESP]
dc.contributor.authorValencia, Carlos Roberto [UNESP]
dc.contributor.authorRoberto, Guilherme Freire [UNESP]
dc.contributor.authorDonega Zafalon, Geraldo Francisco [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-11-29T20:36:17Z
dc.date.available2018-11-29T20:36:17Z
dc.date.issued2018-08-01
dc.description.abstractMultiple sequence alignment (MSA) is one of the most important tasks in bioinformatics and it can be used to prediction of structures or functions of unknown proteins and to phylogenetic tree reconstruction. There are many heuristics to perform multiple sequence alignment, as Progressive Alignment, Ant Colony, Genetic Algorithms, among others. Along the years, some tools were proposed to perform MSA and MSA-GA is one of them. The MSA-GA is a tool based on Genetic Algorithm to perform multiple sequence alignment and its results are generally better than other well-known tools in bioinformatics, as Clustal W. The COFFEE objective function was implemented in the MSA-GA in order to allow it to produce better alignments to less similar sequence sets of proteins. Nonetheless, the COFFEE objective function is not suited do perform multiple sequence alignment of nucleotides. Thus, we have modified the COFFEE objective function, previously implemented in the MSA-GA, to allow it to obtain better results also to sequences of nucleotides. Our results have shown that our approach has achieved better results in all cases when compared with standard COFFEE and most of cases when compared with WSP for all test cases from BAliBase and BRAliBase. Moreover, our results are more reliable because their standard deviations have less variation. (C) 2018 Elsevier Ltd. All rights reserved.en
dc.description.affiliationSao Paulo State Univ, Dept Comp Sci & Stat, Rua Cristovao Colombo 2265, Sao Paulo, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Dept Comp Sci & Stat, Rua Cristovao Colombo 2265, Sao Paulo, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 13/08289-0
dc.format.extent39-44
dc.identifierhttp://dx.doi.org/10.1016/j.compbiolchem.2018.04.012
dc.identifier.citationComputational Biology And Chemistry. Oxford: Elsevier Sci Ltd, v. 75, p. 39-44, 2018.
dc.identifier.doi10.1016/j.compbiolchem.2018.04.012
dc.identifier.fileWOS000437057500005.pdf
dc.identifier.issn1476-9271
dc.identifier.lattes2139053814879312
dc.identifier.urihttp://hdl.handle.net/11449/166222
dc.identifier.wosWOS:000437057500005
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofComputational Biology And Chemistry
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectMultiple sequence alignment
dc.subjectGenetic Algorithm
dc.subjectOptimization
dc.titleAn approach for COFFEE objective function to global DNA multiple sequence alignmenten
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
dspace.entity.typePublication
unesp.author.lattes2139053814879312
unesp.author.orcid0000-0001-8580-7054[2]
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Biociências Letras e Ciências Exatas, São José do Rio Pretopt
unesp.departmentCiências da Computação e Estatística - IBILCEpt

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