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A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment
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| 2.63 MB | Adobe PDF |
Orientador(es)
Resumo(s)
The alignment among three or more nucleotides/amino acids sequences at the same time is known as multiple sequence alignment (MSA), a nondeterministic polynomial time (NP)-hard optimization problem. The time complexity of finding an optimal alignment raises exponentially when the number of sequences to align increases. In this work, we deal with a multiobjective version of the MSA problem wherein the goal is to simultaneously optimize the accuracy and conservation of the alignment. A parallel version of the hybrid multiobjective memetic metaheuristics for MSA is proposed. To evaluate the parallel performance of our proposal, we have selected a pull of data sets with different number of sequences (up to 1000 sequences) and study its parallel performance against other well-known parallel metaheuristics published in the literature, such as MSAProbs, tree-based consistency objective function for alignment evaluation (T-Coffee), Clustal Ω, and multiple alignment using fast Fourier transform (MAFFT). The comparative study reveals that our parallel aligner obtains better results than MSAProbs, T-Coffee, Clustal Ω, and MAFFT. In addition, the parallel version is around 25 times faster than the sequential version with 32 cores, obtaining an efficiency around 80%.
Descrição
Rubio-Largo, Á., Castelli, M., Vanneschi, L., & Vega-Rodríguez, M. A. (2018). A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment. Journal of Computational Biology, 25(9), 1009-1022. DOI: 10.1089/cmb.2018.0031
Palavras-chave
Memetic computing Metaheuristic Multiobjective optimization Multiple sequence alignment Modelling and Simulation Molecular Biology Genetics Computational Mathematics Computational Theory and Mathematics SDG 3 - Good Health and Well-being