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Related Experiment Videos

Multiple alignment by aligning alignments.

Travis J Wheeler1, John D Kececioglu

  • 1Department of Computer Science, The University of Arizona, Tucson, AZ 85721, USA. twheeler@cs.arizona.edu

Bioinformatics (Oxford, England)
|July 25, 2007
PubMed
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This study introduces Opal, a new multiple sequence alignment tool. Opal achieves top-tier alignment quality by optimizing each stage of the form-and-polish strategy, particularly distance estimation and polishing.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple sequence alignment is crucial in bioinformatics.
  • Existing tools use a form-and-polish strategy involving subalignment merging and refinement.
  • Optimizing each stage of this strategy is key to improving alignment quality.

Purpose of the Study:

  • To investigate optimal methods for each stage of the form-and-polish multiple sequence alignment strategy.
  • To develop a new algorithm for optimally merging subalignments.
  • To create a high-quality multiple sequence alignment tool.

Main Methods:

  • Analyzed six stages of the form-and-polish strategy: parameter choice, distance estimation, merge-tree construction, sequence-pair weighting, alignment merging, and polishing.

Related Experiment Videos

  • Developed novel approaches for distance estimation using normalized alignment costs and for polishing using 3-cuts.
  • Explored an input-dependent parameter-value oracle for alignment parameter selection.

    • Main Results:

    • Novel methods for distance estimation and polishing significantly improved alignment quality.
    • Combining optimized methods resulted in the Opal tool, which matches top tools on benchmark alignments.
    • Opal achieves high quality without using alignment consistency or hydrophobic gap penalties.

    Conclusions:

    • Optimizing individual stages, especially distance estimation and polishing, yields substantial improvements in multiple sequence alignment.
    • The Opal tool represents a significant advancement in multiple sequence alignment quality and efficiency.
    • Further gains are possible through input-dependent parameter optimization.