A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment

Álvaro Rubio-Largo, Mauro Castelli, Leonardo Vanneschi, Miguel A. Vega-Rodríguez

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
32 Downloads (Pure)

Abstract

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%.

Original languageEnglish
Pages (from-to)1009-1022
Number of pages14
JournalJournal of Computational Biology
Volume25
Issue number9
DOIs
Publication statusPublished - 1 Sept 2018

Keywords

  • Memetic computing
  • Metaheuristic
  • Multiobjective optimization
  • Multiple sequence alignment

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