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A non-local gap-penalty for profile alignment

W R Taylor1

  • 1Division of Mathematical Biology, National Institute for Medical Research, London, UK.

Bulletin of Mathematical Biology
|January 1, 1996
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel algorithm to correct bias in biological sequence alignment profiles. The method improves accuracy for alignments with many gaps, enhancing profile-based sequence alignment.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Sequence alignments often exceed the mean length of constituent sequences due to gap insertions.
  • Using gapped alignments as profiles can introduce bias, favoring sequences that match profile length over mean sequence length.

Purpose of the Study:

  • To develop and evaluate an algorithm that corrects for length bias in profile-based sequence alignment.
  • To improve the accuracy of aligning new sequences to existing profiles, especially those with significant gaps.

Main Methods:

  • A novel algorithm was developed to monitor mean separations between aligned positions in profiles.
  • This correction was integrated into a dynamic programming algorithm by modifying the gap penalty in a non-local manner.
  • The algorithm's performance was tested on protein families using parameter space analysis.

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Main Results:

  • The algorithm demonstrated significant improvements for highly gapped alignments, increasing alignment accuracy.
  • A 50% increase in parameter space area was observed for one protein family.
  • The method successfully aligned a protein family that was unalignable using unbiased methods.

Conclusions:

  • The developed algorithm effectively corrects length bias in profile-based sequence alignment.
  • This approach offers substantial benefits for aligning sequences with extensive gaps.
  • The non-local gap penalty modification provides a more robust alignment strategy.