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Multiple sequence alignment by parallel simulated annealing

M Ishikawa1, T Toya, M Hoshida

  • 1Institute for New Generation Computer Technology (ICOT), Tokyo, Japan.

Computer Applications in the Biosciences : CABIOS
|June 1, 1993
PubMed
Summary
This summary is machine-generated.

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We developed simulated annealing algorithms for optimal multiple sequence alignment. Parallel algorithms achieved better results than conventional methods in reasonable times, with the temperature parallel algorithm being most suitable.

Area of Science:

  • Computational Biology
  • Bioinformatics

Background:

  • Multiple sequence alignment (MSA) is crucial for understanding protein and nucleic acid evolution.
  • Traditional MSA algorithms can be computationally intensive, especially for large datasets.

Purpose of the Study:

  • To develop and evaluate simulated annealing algorithms for solving the multiple sequence alignment problem.
  • To improve the efficiency and accuracy of MSA through parallel computing.

Main Methods:

  • Development of sequential and parallel simulated annealing algorithms (including a temperature parallel algorithm).
  • Comparison of annealing algorithms against a conventional tree-based algorithm using dynamic programming.
  • Validation of optimality for three-sequence alignment using a rigorous dynamic programming approach.

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Main Results:

  • All simulated annealing algorithms outperformed the conventional tree-based method in terms of energy value.
  • Parallel simulated annealing algorithms found optimal or near-optimal solutions within practical timeframes.
  • The temperature parallel algorithm demonstrated superior performance without requiring complex scheduling.

Conclusions:

  • Parallel simulated annealing, particularly the temperature parallel variant, is a highly effective method for achieving optimal multiple sequence alignment.
  • This approach offers a significant improvement over existing heuristic and conventional algorithms.
  • The developed algorithms are also valuable for refining existing multiple sequence alignments.