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Related Experiment Videos

A word-oriented approach to alignment validation.

Robert G Beiko1, Cheong Xin Chan, Mark A Ragan

  • 1ARC Centre in Bioinformatics and Institute for Molecular Bioscience, The University of Queensland Brisbane, Qld 4072, Australia.

Bioinformatics (Oxford, England)
|February 25, 2005
PubMed
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We developed WOOF, a novel objective function for automated protein sequence alignment. WOOF effectively identifies conserved patterns to select the best alignment from multiple algorithms, improving accuracy in large-scale proteome analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Automated whole-proteome multiple sequence alignment lacks manual validation.
  • Existing evolutionary models are too general for optimal residue-level alignment.
  • A novel objective function is needed to select the best alignment from multiple algorithms.

Purpose of the Study:

  • To introduce WOOF, a novel 'word-oriented' objective function for automated protein sequence alignment.
  • To evaluate WOOF's effectiveness in selecting biologically optimal alignments.
  • To provide a scalable solution for large-scale proteome analysis.

Main Methods:

  • A 'shotgun' strategy using multiple alignment algorithms.
  • Development of the WOOF (word-oriented) objective function.

Related Experiment Videos

  • Identification and scoring of conserved amino acid patterns (words).

    • Main Results:

    • WOOF ranked manually curated reference alignments highest in a majority of protein families.
    • A strong positive correlation was observed between WOOF scores and similarity to reference alignments.
    • WOOF demonstrated speed and independence from 3D structure, suitable for large-scale analysis.

    Conclusions:

    • WOOF is a reliable objective function for selecting optimal automated protein sequence alignments.
    • The 'shotgun' approach combined with WOOF improves alignment accuracy.
    • WOOF facilitates efficient analysis of large protein family datasets.