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Related Experiment Videos

Evolutionary HMMs: a Bayesian approach to multiple alignment.

I Holmes1, W J Bruno

  • 1Group T10, Los Alamos National Laboratory, NM 87545, USA. ihh@fruitfly.org

Bioinformatics (Oxford, England)
|October 9, 2001
PubMed
Summary
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We developed a new algorithm for Bayesian multiple sequence alignment, which is computationally efficient and comparable to existing methods. The Handel software implementation is freely available for DNA and protein sequence analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Review of probabilistic methods for sequence alignment, profiling, and phylogeny.
  • Development of a novel multiple alignment algorithm based on Bayesian inference within the links model.
  • Algorithm samples and maximizes posterior distribution for multiple sequence alignments, conditioned on a phylogenetic tree.

Purpose of the Study:

  • To present a software implementation (Handel) of the developed multiple alignment algorithm.
  • To evaluate the performance of Handel using simulated data and benchmark protein alignments (BAliBASE).
  • To discuss potential improvements to the links model and its implications for Bayesian multiple alignment and phylogenetic profiling.

Main Methods:

  • Implementation of a multiple alignment algorithm for Bayesian inference in the links model.

Related Experiment Videos

  • Utilizing sampling and maximization of the posterior distribution over multiple alignments.
  • Testing the algorithm with simulated datasets and BAliBASE protein alignments.

    • Main Results:

    • The Handel algorithm achieves a mean sum-of-pairs score only 13% lower than CLUSTALW on BAliBASE alignments.
    • The algorithm's computational requirements for sampling and maximization are comparable to pairwise alignment.
    • Comparison highlights the relative simplicity of the links model versus CLUSTALW's complex scoring system.

    Conclusions:

    • The Handel algorithm provides a computationally efficient approach to Bayesian multiple sequence alignment.
    • The software implementation demonstrates competitive performance against established methods like CLUSTALW.
    • Findings suggest avenues for refining the links model for enhanced phylogenetic profiling and Bayesian alignment.