A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment
Autor(a) principal: | |
---|---|
Data de Publicação: | 2018 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://doi.org/10.1101/103101 |
Resumo: | Rubio-Largo, Á., Castelli, M., Vanneschi, L., & Vega-Rodríguez, M. A. (2018). A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment. Journal of Computational Biology, 25(9), 1009-1022. DOI: 10.1089/cmb.2018.0031 |
id |
RCAP_f3ddbc0b91151b44c78c2cd1d3a5ef08 |
---|---|
oai_identifier_str |
oai:run.unl.pt:10362/47032 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
A Parallel Multiobjective Metaheuristic for Multiple Sequence AlignmentMemetic computingMetaheuristicMultiobjective optimizationMultiple sequence alignmentModelling and SimulationMolecular BiologyGeneticsComputational MathematicsComputational Theory and MathematicsSDG 3 - Good Health and Well-beingRubio-Largo, Á., Castelli, M., Vanneschi, L., & Vega-Rodríguez, M. A. (2018). A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment. Journal of Computational Biology, 25(9), 1009-1022. DOI: 10.1089/cmb.2018.0031The alignment among three or more nucleotides/amino acids sequences at the same time is known as multiple sequence alignment (MSA), a nondeterministic polynomial time (NP)-hard optimization problem. The time complexity of finding an optimal alignment raises exponentially when the number of sequences to align increases. In this work, we deal with a multiobjective version of the MSA problem wherein the goal is to simultaneously optimize the accuracy and conservation of the alignment. A parallel version of the hybrid multiobjective memetic metaheuristics for MSA is proposed. To evaluate the parallel performance of our proposal, we have selected a pull of data sets with different number of sequences (up to 1000 sequences) and study its parallel performance against other well-known parallel metaheuristics published in the literature, such as MSAProbs, tree-based consistency objective function for alignment evaluation (T-Coffee), Clustal Ω, and multiple alignment using fast Fourier transform (MAFFT). The comparative study reveals that our parallel aligner obtains better results than MSAProbs, T-Coffee, Clustal Ω, and MAFFT. In addition, the parallel version is around 25 times faster than the sequential version with 32 cores, obtaining an efficiency around 80%.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNRubio-Largo, ÁlvaroCastelli, MauroVanneschi, LeonardoVega-Rodríguez, Miguel A.2018-09-20T22:20:25Z2018-09-012018-09-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article14application/pdfapplication/pdfhttps://doi.org/10.1101/103101eng1066-5277PURE: 5868267http://www.scopus.com/inward/record.url?scp=85053186520&partnerID=8YFLogxKhttps://doi.org/10.1101/103101info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:24:37Zoai:run.unl.pt:10362/47032Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:32:02.620962Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment |
title |
A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment |
spellingShingle |
A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment Rubio-Largo, Álvaro Memetic computing Metaheuristic Multiobjective optimization Multiple sequence alignment Modelling and Simulation Molecular Biology Genetics Computational Mathematics Computational Theory and Mathematics SDG 3 - Good Health and Well-being |
title_short |
A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment |
title_full |
A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment |
title_fullStr |
A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment |
title_full_unstemmed |
A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment |
title_sort |
A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment |
author |
Rubio-Largo, Álvaro |
author_facet |
Rubio-Largo, Álvaro Castelli, Mauro Vanneschi, Leonardo Vega-Rodríguez, Miguel A. |
author_role |
author |
author2 |
Castelli, Mauro Vanneschi, Leonardo Vega-Rodríguez, Miguel A. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School RUN |
dc.contributor.author.fl_str_mv |
Rubio-Largo, Álvaro Castelli, Mauro Vanneschi, Leonardo Vega-Rodríguez, Miguel A. |
dc.subject.por.fl_str_mv |
Memetic computing Metaheuristic Multiobjective optimization Multiple sequence alignment Modelling and Simulation Molecular Biology Genetics Computational Mathematics Computational Theory and Mathematics SDG 3 - Good Health and Well-being |
topic |
Memetic computing Metaheuristic Multiobjective optimization Multiple sequence alignment Modelling and Simulation Molecular Biology Genetics Computational Mathematics Computational Theory and Mathematics SDG 3 - Good Health and Well-being |
description |
Rubio-Largo, Á., Castelli, M., Vanneschi, L., & Vega-Rodríguez, M. A. (2018). A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment. Journal of Computational Biology, 25(9), 1009-1022. DOI: 10.1089/cmb.2018.0031 |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-09-20T22:20:25Z 2018-09-01 2018-09-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://doi.org/10.1101/103101 |
url |
https://doi.org/10.1101/103101 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1066-5277 PURE: 5868267 http://www.scopus.com/inward/record.url?scp=85053186520&partnerID=8YFLogxK https://doi.org/10.1101/103101 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
14 application/pdf application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799137942726246400 |