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An evolutionary approach for performing multiple sequence alignment

2010-07Conference paper Restricted access
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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosCiências Naturais::Matemáticas
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg07:Energias Renováveis e Acessíveis
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
dc.contributor.authorSilva, Fernando José Mateus
dc.contributor.authorSánchez-Pérez, Juan Manuel
dc.contributor.authorGómez-Pulido, Juan Antonio
dc.contributor.authorVega-Rodríguez, Miguel A.
dc.date.accessioned2025-12-16T15:45:11Z
dc.date.available2025-12-16T15:45:11Z
dc.date.issued2010-07
dc.descriptionEISBN - 978-1-4244-6911-6
dc.descriptionDate of Conference: 18-23 July 2010
dc.description.abstractDespite of being a very common task in bioinformatics, multiple sequence alignment is not a trivial matter. Arranging a set of molecular sequences to reveal their similarities and their differences is often hardened by the complexity and the size of the search space involved, which undermine the approaches that try to explore exhaustively the solution's search space. Due to its nature, Genetic Algorithms, which are prone for general combinatorial problems optimization in large and complex search spaces, emerge as serious candidates to tackle with the multiple sequence alignment problem. We have developed an evolutionary approach, AlineaGA, which uses a Genetic Algorithm with local search optimization embedded on its mutation operators for performing multiple sequence alignment. Now, we have enhanced its selection method by employing an elitist strategy, and we have also developed a new crossover operator. These transformations allow AlineaGA to improve its robustness and to obtain better fit solutions. Also, we have studied the effect of the mutation probability in solutions' evolution by analyzing the performance of the whole population throughout generations. We conclude that increasing the mutation probability leads to better solutions in fewer generations and that the mutation operators have a dramatic effect in this particular domain.eng
dc.description.sponsorshipThis work has been partially supported by the Polytechnic Institute of Leiria (Portugal) and the MSTAR project Reference: TIN2008-06491-C04-04/TIN ( MICINN Spain).
dc.identifier.citationF. J. M. Silva, J. M. Sánchez-Pérez, J. A. Gómez-Pulido and M. A. Vega-Rodríguez, "An evolutionary approach for performing multiple sequence alignment," IEEE Congress on Evolutionary Computation, Barcelona, Spain, 2010, pp. 1-7, doi: https://doi.org/10.1109/CEC.2010.5586500.
dc.identifier.doi10.1109/cec.2010.5586500
dc.identifier.isbn978-1-4244-6909-3
dc.identifier.isbn978-1-4244-6911-6
dc.identifier.urihttp://hdl.handle.net/10400.8/15091
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE Canada
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/5586500
dc.relation.ispartofIEEE Congress on Evolutionary Computation
dc.rights.uriN/A
dc.subjectOptimization
dc.subjectAmino acids
dc.subjectSearch problems
dc.subjectEvolution (biology)
dc.subjectRobustness
dc.subjectConvergence
dc.titleAn evolutionary approach for performing multiple sequence alignmenteng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2010-07
oaire.citation.conferencePlaceBarcelona, Spain
oaire.citation.title2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameSilva
person.givenNameFernando
person.identifier.ciencia-id9D19-84F9-F1CA
person.identifier.orcid0000-0001-9335-1851
person.identifier.scopus-author-id24402946400
relation.isAuthorOfPublication2db213d9-a071-4f43-9544-1295ebb6ffde
relation.isAuthorOfPublication.latestForDiscovery2db213d9-a071-4f43-9544-1295ebb6ffde

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Despite of being a very common task in bioinformatics, multiple sequence alignment is not a trivial matter. Arranging a set of molecular sequences to reveal their similarities and their differences is often hardened by the complexity and the size of the search space involved, which undermine the approaches that try to explore exhaustively the solution's search space. Due to its nature, Genetic Algorithms, which are prone for general combinatorial problems optimization in large and complex search spaces, emerge as serious candidates to tackle with the multiple sequence alignment problem. We have developed an evolutionary approach, AlineaGA, which uses a Genetic Algorithm with local search optimization embedded on its mutation operators for performing multiple sequence alignment. Now, we have enhanced its selection method by employing an elitist strategy, and we have also developed a new crossover operator. These transformations allow AlineaGA to improve its robustness and to obtain better fit solutions. Also, we have studied the effect of the mutation probability in solutions' evolution by analyzing the performance of the whole population throughout generations. We conclude that increasing the mutation probability leads to better solutions in fewer generations and that the mutation operators have a dramatic effect in this particular domain.
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