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A sequence sub-sampling algorithm increases the power to detect distant homologues.

Catrióna R Johnston1, Denis C Shields

  • 1Department of Clinical Pharmacology, Bioinformatics Group, Royal College of Surgeons in Ireland, 123 St Stephens Green, Dublin 2, Ireland. kjohnston@rcsi.ie

Nucleic Acids Research
|July 12, 2005
PubMed
Summary
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New algorithms improve the detection of distant protein homologues by analyzing sequence alignments. These methods enhance sensitivity and accuracy, outperforming existing tools, especially with complex evolutionary patterns.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Homology detection is crucial for understanding protein function and evolution.
  • Current methods often assume regular protein evolution, which can limit distant homologue detection.
  • Existing tools like HMMER and BLAST may struggle with complex evolutionary patterns and alignment errors.

Purpose of the Study:

  • To develop and evaluate novel algorithms for enhanced distant homologue detection.
  • To improve the sensitivity and accuracy of homology searches using sequence alignments.
  • To address limitations of existing methods in handling irregular evolutionary patterns.

Main Methods:

  • Investigated combined HMMER search results from random alignment subsets (Rand-shuffle algorithm).

Related Experiment Videos

  • Utilized the SCOP structural classification to validate true similarities.
  • Developed an extension, Ali-shuffle, weighting more informative sequence subsets.

    • Main Results:

    • Rand-shuffle algorithm increased HMMER sensitivity by 37.5% at a 5% false-positive rate, excluding BLAST-identifiable similarities.
    • Ali-shuffle outperformed HMMER and PSI-BLAST, particularly at higher false-positive rates.
    • Sequence sub-sampling methods showed improved performance, potentially due to reduced sensitivity to alignment errors and irregular evolution.

    Conclusions:

    • Novel sequence sub-sampling algorithms (Rand-shuffle and Ali-shuffle) significantly enhance distant homologue detection.
    • These methods offer improved sensitivity and robustness compared to existing tools.
    • The algorithms are valuable for uncovering evolutionary relationships in challenging datasets.