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Approximate statistics of gapped alignments.

R Mott1, R Tribe

  • 1Wellcome Trust Centre for Human Genetics, Oxford, UK. Richard.Mott@well.ox.ac.uk

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|May 1, 1999
PubMed
Summary

A new method approximates gapped alignment scores for random protein sequences. This score distribution is governed by a single parameter, simplifying analysis of sequence comparisons.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Sequence Analysis

Background:

  • Gapped sequence alignment is crucial for comparing protein sequences.
  • Understanding the statistical distribution of alignment scores is essential for reliable biological interpretation.
  • Existing methods may be computationally intensive or lack accuracy for certain parameters.

Purpose of the Study:

  • To develop a heuristic approximation for the score distribution of gapped alignments in the logarithmic domain.
  • To provide a method applicable to random, unrelated protein sequences with standard scoring matrices and arbitrary gap penalties.
  • To identify key parameters governing gapped alignment behavior and predict score distribution transitions.

Main Methods:

  • Developed a heuristic approximation for gapped alignment score distributions.

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  • Analyzed behavior for random protein sequences using standard score matrices and arbitrary gap penalties.
  • Utilized simulation to test the accuracy of the approximation.

    • Main Results:

    • The score distribution of gapped alignments is effectively governed by a single parameter, alpha.
    • Parameter alpha depends on the gap penalty scheme and sequence composition.
    • The approximation accurately predicts the transition point between logarithmic and linear score distribution behavior.
    • Simulations confirm the approximation's accuracy across various substitution matrices and gap penalties.

    Conclusions:

    • A computationally efficient and accurate approximation for gapped alignment score distributions is presented.
    • The findings simplify the statistical analysis of protein sequence comparisons.
    • This method enhances the reliability of identifying homologous sequences through gapped alignments.