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M2Align: parallel multiple sequence alignment with a multi-objective metaheuristic
Author(s) -
Cristian ZambranoVega,
Antonio J. Nebro,
José García-Nieto,
José F. AldanaMontes
Publication year - 2017
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btx338
Subject(s) - computer science , benchmark (surveying) , metaheuristic , sequence (biology) , multiple sequence alignment , source code , code (set theory) , algorithm , software , execution time , mathematical optimization , encoding (memory) , sequence alignment , parallel computing , artificial intelligence , mathematics , set (abstract data type) , biochemistry , chemistry , geodesy , biology , gene , peptide sequence , genetics , programming language , geography , operating system
Multiple sequence alignment (MSA) is an NP-complete optimization problem found in computational biology, where the time complexity of finding an optimal alignment raises exponentially along with the number of sequences and their lengths. Additionally, to assess the quality of a MSA, a number of objectives can be taken into account, such as maximizing the sum-of-pairs, maximizing the totally conserved columns, minimizing the number of gaps, or maximizing structural information based scores such as STRIKE. An approach to deal with MSA problems is to use multi-objective metaheuristics, which are non-exact stochastic optimization methods that can produce high quality solutions to complex problems having two or more objectives to be optimized at the same time. Our motivation is to provide a multi-objective metaheuristic for MSA that can run in parallel taking advantage of multi-core-based computers.

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