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Parallelization of the MAFFT multiple sequence alignment program.

Kazutaka Katoh1, Hiroyuki Toh

  • 1Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan. kazutaka.katoh@aist.go.jp

Bioinformatics (Oxford, England)
|April 30, 2010
PubMed
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This study parallelized the MAFFT multiple sequence alignment (MSA) program for faster large-scale sequence analyses. A simple hill-climbing strategy was chosen for iterative refinement, improving computational efficiency.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple sequence alignment (MSA) is crucial for comparative sequence analysis.
  • Large-scale sequence datasets necessitate efficient computational methods.
  • The MAFFT program is a widely used tool for MSA.

Purpose of the Study:

  • To parallelize the MAFFT program to accelerate large-scale sequence analyses.
  • To implement and evaluate different parallelization strategies for MSA.
  • To optimize the performance of the MAFFT algorithm.

Main Methods:

  • Parallelization of MAFFT's three core stages (all-to-all comparison, progressive alignment, iterative refinement) using POSIX Threads.
  • Implementation of two parallelization strategies for the iterative refinement stage: best-first and simple hill-climbing.

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  • Performance evaluation based on objective and benchmark scores.

    • Main Results:

    • Successful parallelization of the MAFFT program.
    • Demonstrated reduction in computation time for large-scale sequence alignments.
    • Identified simple hill-climbing as a more effective strategy for iterative refinement.

    Conclusions:

    • Parallelization significantly enhances the efficiency of MAFFT for large-scale sequence analysis.
    • The chosen simple hill-climbing approach optimizes the performance of the iterative refinement stage.
    • The parallelized MAFFT version offers a valuable tool for computational biology research.