A Hybrid Approach using Progressive and Genetic Algorithms for Improvements in Multiple Sequence Alignments
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.5220/0010495303840391 http://hdl.handle.net/11449/237714 |
Resumo: | The multiple sequence alignment is one of the main tasks in bioinformatics. It is used in different important biological analysis, such as function and structure prediction of unknown proteins. There are several approaches to perform multiple sequence alignment and the use of heuristics and meta-heuristics stands out because of the search ability of these methods, which generally leads to good results in a reasonable amount of time. The progressive alignment and genetic algorithm are among the most used heuristics and meta-heuristics to perform multiple sequence alignment. However, both methods have disadvantages, such as error propagation in the case of progressive alignment and local optima results in the case of genetics algorithm. Thus, this work proposes a new hybrid refinement phase using a progressive approach to locally realign the multiple sequence alignment produced by genetic algorithm based tools. Our results show that our method is able to improve the quality of the alignments of all families from BAliBase. Considering Q and TC quality measures from BaliBase, we have obtained the improvements of 55% for Q and 167% for TC. Then, with these results we can provide more biologically significant results. |
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A Hybrid Approach using Progressive and Genetic Algorithms for Improvements in Multiple Sequence AlignmentsGenetic AlgorithmMultiple Sequence AlignmentHybrid Multiple Sequence AlignmentBioinformaticsThe multiple sequence alignment is one of the main tasks in bioinformatics. It is used in different important biological analysis, such as function and structure prediction of unknown proteins. There are several approaches to perform multiple sequence alignment and the use of heuristics and meta-heuristics stands out because of the search ability of these methods, which generally leads to good results in a reasonable amount of time. The progressive alignment and genetic algorithm are among the most used heuristics and meta-heuristics to perform multiple sequence alignment. However, both methods have disadvantages, such as error propagation in the case of progressive alignment and local optima results in the case of genetics algorithm. Thus, this work proposes a new hybrid refinement phase using a progressive approach to locally realign the multiple sequence alignment produced by genetic algorithm based tools. Our results show that our method is able to improve the quality of the alignments of all families from BAliBase. Considering Q and TC quality measures from BaliBase, we have obtained the improvements of 55% for Q and 167% for TC. Then, with these results we can provide more biologically significant results.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Universidade Paulista (Unip/ICET)Univ Estadual Paulista, UNESP, Dept Comp Sci & Stat, Rua Cristovao Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP, BrazilUniv Sao Paulo, Dept Comp & Digital Syst Engn, Escola Politecn, Av Prof Luciano Gualberto,Travessa 3,158, BR-05508010 Sao Paulo, SP, BrazilUniv Paulista, Dept ICET, Ave Presidente Juscelino Kubitschek de Oliveira, BR-15091450 Sao Jose Do Rio Preto, SP, BrazilUniv Estadual Paulista, UNESP, Dept Comp Sci & Stat, Rua Cristovao Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP, BrazilFAPESP: 2019/00030-3Universidade Paulista (Unip/ICET): 7-03/1116/2019ScitepressUniversidade Estadual Paulista (UNESP)Universidade de São Paulo (USP)Univ PaulistaZafalon, Geraldo Francisco Donega[UNESP]Gomes, Vitoria Zanon [UNESP]Amorim, Anderson Rici [UNESP]Valencio, Carlos Roberto [UNESP]Filipe, J.Smialek, M.Brodsky, A.Hammoudi, S.2022-11-30T13:42:45Z2022-11-30T13:42:45Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject384-391http://dx.doi.org/10.5220/0010495303840391Iceis: Proceedings Of The 23rd International Conference On Enterprise Information Systems - Vol 2. Setubal: Scitepress, p. 384-391, 2021.2184-4992http://hdl.handle.net/11449/23771410.5220/0010495303840391WOS:000783951300040Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIceis: Proceedings Of The 23rd International Conference On Enterprise Information Systems - Vol 2info:eu-repo/semantics/openAccess2022-11-30T13:42:45Zoai:repositorio.unesp.br:11449/237714Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:25:21.181671Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A Hybrid Approach using Progressive and Genetic Algorithms for Improvements in Multiple Sequence Alignments |
title |
A Hybrid Approach using Progressive and Genetic Algorithms for Improvements in Multiple Sequence Alignments |
spellingShingle |
A Hybrid Approach using Progressive and Genetic Algorithms for Improvements in Multiple Sequence Alignments Zafalon, Geraldo Francisco Donega[UNESP] Genetic Algorithm Multiple Sequence Alignment Hybrid Multiple Sequence Alignment Bioinformatics |
title_short |
A Hybrid Approach using Progressive and Genetic Algorithms for Improvements in Multiple Sequence Alignments |
title_full |
A Hybrid Approach using Progressive and Genetic Algorithms for Improvements in Multiple Sequence Alignments |
title_fullStr |
A Hybrid Approach using Progressive and Genetic Algorithms for Improvements in Multiple Sequence Alignments |
title_full_unstemmed |
A Hybrid Approach using Progressive and Genetic Algorithms for Improvements in Multiple Sequence Alignments |
title_sort |
A Hybrid Approach using Progressive and Genetic Algorithms for Improvements in Multiple Sequence Alignments |
author |
Zafalon, Geraldo Francisco Donega[UNESP] |
author_facet |
Zafalon, Geraldo Francisco Donega[UNESP] Gomes, Vitoria Zanon [UNESP] Amorim, Anderson Rici [UNESP] Valencio, Carlos Roberto [UNESP] Filipe, J. Smialek, M. Brodsky, A. Hammoudi, S. |
author_role |
author |
author2 |
Gomes, Vitoria Zanon [UNESP] Amorim, Anderson Rici [UNESP] Valencio, Carlos Roberto [UNESP] Filipe, J. Smialek, M. Brodsky, A. Hammoudi, S. |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Universidade de São Paulo (USP) Univ Paulista |
dc.contributor.author.fl_str_mv |
Zafalon, Geraldo Francisco Donega[UNESP] Gomes, Vitoria Zanon [UNESP] Amorim, Anderson Rici [UNESP] Valencio, Carlos Roberto [UNESP] Filipe, J. Smialek, M. Brodsky, A. Hammoudi, S. |
dc.subject.por.fl_str_mv |
Genetic Algorithm Multiple Sequence Alignment Hybrid Multiple Sequence Alignment Bioinformatics |
topic |
Genetic Algorithm Multiple Sequence Alignment Hybrid Multiple Sequence Alignment Bioinformatics |
description |
The multiple sequence alignment is one of the main tasks in bioinformatics. It is used in different important biological analysis, such as function and structure prediction of unknown proteins. There are several approaches to perform multiple sequence alignment and the use of heuristics and meta-heuristics stands out because of the search ability of these methods, which generally leads to good results in a reasonable amount of time. The progressive alignment and genetic algorithm are among the most used heuristics and meta-heuristics to perform multiple sequence alignment. However, both methods have disadvantages, such as error propagation in the case of progressive alignment and local optima results in the case of genetics algorithm. Thus, this work proposes a new hybrid refinement phase using a progressive approach to locally realign the multiple sequence alignment produced by genetic algorithm based tools. Our results show that our method is able to improve the quality of the alignments of all families from BAliBase. Considering Q and TC quality measures from BaliBase, we have obtained the improvements of 55% for Q and 167% for TC. Then, with these results we can provide more biologically significant results. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 2022-11-30T13:42:45Z 2022-11-30T13:42:45Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.5220/0010495303840391 Iceis: Proceedings Of The 23rd International Conference On Enterprise Information Systems - Vol 2. Setubal: Scitepress, p. 384-391, 2021. 2184-4992 http://hdl.handle.net/11449/237714 10.5220/0010495303840391 WOS:000783951300040 |
url |
http://dx.doi.org/10.5220/0010495303840391 http://hdl.handle.net/11449/237714 |
identifier_str_mv |
Iceis: Proceedings Of The 23rd International Conference On Enterprise Information Systems - Vol 2. Setubal: Scitepress, p. 384-391, 2021. 2184-4992 10.5220/0010495303840391 WOS:000783951300040 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Iceis: Proceedings Of The 23rd International Conference On Enterprise Information Systems - Vol 2 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
384-391 |
dc.publisher.none.fl_str_mv |
Scitepress |
publisher.none.fl_str_mv |
Scitepress |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128648630239232 |