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Approximations to profile score distributions

L Goldstein1, M S Waterman

  • 1Department of Mathematics, University of Southern California, Los Angeles 90089-1113, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|January 1, 1994
PubMed
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Sequence profiles, used for finding gene families, were analyzed for maximum alignment scores in random sequences. The study shows these scores follow a predictable extreme value distribution, aiding in database searches.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Sequence Analysis

Background:

  • Sequence profiles are essential tools for identifying homologous sequences within biological databases.
  • Current methods rely on accurate statistical models for profile-based sequence searching.

Purpose of the Study:

  • To investigate the statistical properties of maximum alignment scores between sequence profiles and random sequences.
  • To develop a theoretical framework for understanding the performance of profile-based database searches.

Main Methods:

  • Mathematical analysis of profile alignment scores without insertions or deletions (indels).
  • Derivation of an approximation for the distribution function of the maximum alignment score (M).
  • Establishment of an explicit error bound for the derived approximation.

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Main Results:

  • A main theorem provides an approximation to the distribution function of the maximum alignment score (M).
  • An explicit bound on the error of this approximation is established.
  • The study demonstrates that M converges to a limiting extreme value distribution.

Conclusions:

  • The findings provide a robust statistical foundation for interpreting profile alignment scores.
  • The limiting extreme value distribution of M facilitates more accurate significance testing in sequence database searches.
  • This work enhances the reliability of identifying novel sequence family members using profile-based methods.