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Using guide trees to construct multiple-sequence evolutionary HMMs.

I Holmes1

  • 1Department of Statistics, University of Oxford. 1 South Parks Road, Oxford OX1 3TG, UK.

Bioinformatics (Oxford, England)
|July 12, 2003
PubMed
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This study introduces Evolutionary HMMs, a probabilistic model for multiple sequence alignment, and presents algorithms for their construction and dynamic programming. A prototype implementation, Handel, demonstrates its utility.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Score-based progressive alignment uses dynamic programming on guide trees.
  • Evolutionary Hidden Markov Models (HMMs) are probabilistic analogs, combining Pair HMMs on phylogenetic trees for multiple sequence alignment.
  • Current methods face challenges in efficiently handling complex evolutionary relationships.

Purpose of the Study:

  • To present general algorithms for constructing Evolutionary HMMs from Pair HMMs.
  • To develop dynamic programming methods for Multiple-sequence HMMs.
  • To introduce a novel computational framework for advanced sequence analysis.

Main Methods:

  • Development of algorithms for Evolutionary HMM construction.
  • Implementation of dynamic programming for Multiple-sequence HMMs.

Related Experiment Videos

  • Utilizing the Thorne-Kishino-Felsenstein evolutionary model in a prototype system.

    • Main Results:

    • A prototype implementation named Handel was developed.
    • The Handel system demonstrates the practical application of the proposed algorithms.
    • Benchmarking was performed using structural reference alignments to validate performance.

    Conclusions:

    • Evolutionary HMMs provide a robust probabilistic framework for multiple sequence alignment.
    • The developed algorithms and Handel implementation offer efficient computational tools for bioinformatics.
    • This work advances the field of computational evolutionary biology.