Authors:
Geraldo Francisco Donegá Zafalon
1
;
2
;
Vitoria Zanon Gomes
2
;
Anderson Rici Amorim
3
;
2
and
Carlos Roberto Valêncio
2
Affiliations:
1
Department ICET, Universidade Paulista, Avenida Presidente Juscelino Kubitschek de Oliveira, s/n, Jardim Tarraf II, São José do Rio Preto, SP, 15091-450, Brazil
;
2
Department of Computer Science and Statistics, Universidade Estadual Paulista (UNESP), Rua Cristóvão Colombo, 2265, Jardim Nazareth, São José do Rio Preto, SP, 15054-000, Brazil
;
3
Department of Computer and Digital Systems Engineering, Universidade de São Paulo (USP), Escola Politécnica, Av. Prof. Luciano Gualberto, Travessa 3, 158, Butantã, São Paulo, SP, 05508-010, Brazil
Keyword(s):
Genetic Algorithm, Multiple Sequence Alignment, Hybrid Multiple Sequence Alignment, Bioinformatics.
Abstract:
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 alig
nments 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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