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Multiple sequence alignment using simulated annealing

J Kim1, S Pramanik, M J Chung

  • 1Department of Computer Science, Michigan State University, East Lansing 48824-1027.

Computer Applications in the Biosciences : CABIOS
|July 1, 1994
PubMed
Summary
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This study introduces Multiple Sequence Alignment using Simulated Annealing (MSASA), an efficient algorithm for molecular evolution analysis. MSASA overcomes dynamic programming limitations, offering better solutions and faster computation for aligning more sequences.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Evolution

Background:

  • Multiple sequence alignment (MSA) is crucial for studying molecular evolution and structure-sequence relationships.
  • Dynamic programming (DP) is a common MSA method but has limitations regarding gap costs and computational complexity for large datasets.
  • Existing DP methods restrict the number of sequences that can be aligned due to high computational demands.

Purpose of the Study:

  • To develop an efficient multiple sequence alignment algorithm using simulated annealing.
  • To address the limitations of dynamic programming in MSA, particularly concerning gap costs and scalability.
  • To introduce the Multiple Sequence Alignment using Simulated Annealing (MSASA) algorithm.

Main Methods:

  • Developed the Multiple Sequence Alignment using Simulated Annealing (MSASA) algorithm.

Related Experiment Videos

  • Employed simulated annealing as the core optimization technique.
  • Integrated a fast heuristic algorithm to optimize the high-temperature phase, reducing computational complexity.

    • Main Results:

    • MSASA demonstrates significantly reduced computational complexity compared to traditional dynamic programming.
    • The algorithm allows for the use of natural gap costs, leading to potentially improved alignment solutions.
    • MSASA successfully aligns a larger number of sequences and requires less computation time than DP methods.

    Conclusions:

    • MSASA offers a more efficient and flexible approach to multiple sequence alignment.
    • The simulated annealing-based method provides advantages over dynamic programming for analyzing molecular evolution and structure-sequence relationships.
    • MSASA enhances scalability and solution quality in multiple sequence alignment tasks.