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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

An approach for COFFEE objective function to global DNA multiple sequence alignment

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Autor(es):
Amorim, Anderson Rici [1] ; Neves, Leandro Alves [1] ; Valencia, Carlos Roberto [1] ; Roberto, Guilherme Freire [1] ; Donega Zafalon, Geraldo Francisco [1]
Número total de Autores: 5
Afiliação do(s) autor(es):
[1] Sao Paulo State Univ, Dept Comp Sci & Stat, Rua Cristovao Colombo 2265, Sao Paulo - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: COMPUTATIONAL BIOLOGY AND CHEMISTRY; v. 75, p. 39-44, AUG 2018.
Citações Web of Science: 1
Resumo

Multiple sequence alignment (MSA) is one of the most important tasks in bioinformatics and it can be used to prediction of structures or functions of unknown proteins and to phylogenetic tree reconstruction. There are many heuristics to perform multiple sequence alignment, as Progressive Alignment, Ant Colony, Genetic Algorithms, among others. Along the years, some tools were proposed to perform MSA and MSA-GA is one of them. The MSA-GA is a tool based on Genetic Algorithm to perform multiple sequence alignment and its results are generally better than other well-known tools in bioinformatics, as Clustal W. The COFFEE objective function was implemented in the MSA-GA in order to allow it to produce better alignments to less similar sequence sets of proteins. Nonetheless, the COFFEE objective function is not suited do perform multiple sequence alignment of nucleotides. Thus, we have modified the COFFEE objective function, previously implemented in the MSA-GA, to allow it to obtain better results also to sequences of nucleotides. Our results have shown that our approach has achieved better results in all cases when compared with standard COFFEE and most of cases when compared with WSP for all test cases from BAliBase and BRAliBase. Moreover, our results are more reliable because their standard deviations have less variation. (C) 2018 Elsevier Ltd. All rights reserved. (AU)

Processo FAPESP: 13/08289-0 - Alinhamento múltiplo de sequências utilizando algoritmos genéticos com multithreading
Beneficiário:Anderson Rici Amorim
Linha de fomento: Bolsas no Brasil - Iniciação Científica