Improvements in the sensibility of MSA-GA tool using COFFEE objective function

• Autor(a) principal: Amorim, Anderson Rici [UNESP]
• Data de Publicação: 2015
• Outros Autores: Zafalon, Geraldo Francisco Donegá [UNESP], Neves, Leandro Alves [UNESP], Pinto, Alex Sandro Roschildt, Valêncio, Carlos Roberto [UNESP], Machado, José Márcio [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://iopscience.iop.org/article/10.1088/1742-6596/574/1/012104/meta; http://hdl.handle.net/11449/128820

The sequence alignment is one of the most important tasks in Bioinformatics, playing an important role in the sequences analysis. There are many strategies to perform sequence alignment, since those use deterministic algorithms, as dynamic programming, until those ones, which use heuristic algorithms, as Progressive, Ant Colony (ACO), Genetic Algorithms (GA), Simulated Annealing (SA), among others. In this work, we have implemented the objective function COFFEE in the MSA-GA tool, in substitution of Weighted Sum-of-Pairs (WSP), to improve the final results. In the tests, we were able to verify the approach using COFFEE function achieved better results in 81% of the lower similarity alignments when compared with WSP approach. Moreover, even in the tests with more similar sets, the approach using COFFEE was better in 43% of the times.