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A survey of multiple sequence comparison methods.

S C Chan1, A K Wong, D K Chiu

  • 1Department of Systems Design Engineering, University of Waterloo, Canada.

Bulletin of Mathematical Biology
|July 1, 1992
PubMed
Summary
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This study surveys methods for comparing multiple macromolecular sequences, focusing on heuristic approaches. It introduces novel distance measures and proposes entropy for evaluating alignment optimality without prior sequence knowledge.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Sequence Analysis

Background:

  • Multiple sequence comparison is crucial for understanding biological relationships.
  • Existing methods for comparing three or more macromolecular sequences include exhaustive and heuristic approaches.
  • Heuristic methods offer practical solutions for complex sequence comparisons.

Purpose of the Study:

  • To survey and categorize exhaustive and heuristic methods for multiple macromolecular sequence comparison.
  • To introduce new distance measures for comparing sequence alignments.
  • To propose entropy as a metric for evaluating alignment optimality.

Main Methods:

  • Review of existing literature on exhaustive and heuristic multiple sequence alignment algorithms.

Related Experiment Videos

  • Development of four distance measures based on information engineering and genetic principles.
  • Application of information entropy for assessing alignment quality.

    • Main Results:

    • A comprehensive overview of heuristic methods for multiple sequence comparison is presented.
    • Four novel distance measures are introduced for comparing sequence alignments.
    • Entropy is demonstrated as an effective measure for evaluating alignment optimality, particularly when sequence structures are unknown.

    Conclusions:

    • Heuristic methods are vital for practical multiple sequence comparison.
    • The proposed distance measures and entropy-based evaluation offer valuable tools for sequence analysis.
    • This work provides insights into optimizing sequence alignment strategies in bioinformatics.