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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Amorim, Anderson Rici [1] ; Neves, Leandro Alves [1] ; Valencia, Carlos Roberto [1] ; Roberto, Guilherme Freire [1] ; Donega Zafalon, Geraldo Francisco [1]
Total Authors: 5
[1] Sao Paulo State Univ, Dept Comp Sci & Stat, Rua Cristovao Colombo 2265, Sao Paulo - Brazil
Total Affiliations: 1
Document type: Journal article
Web of Science Citations: 1

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)

FAPESP's process: 13/08289-0 - Multiple sequence alignment using genetic algorithms with multithreading
Grantee:Anderson Rici Amorim
Support type: Scholarships in Brazil - Scientific Initiation