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Related Experiment Videos

Accelerated likelihood surface exploration: the likelihood ratchet.

R A Vos1

  • 1Department of Biological Sciences, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia V5A 1S6, Canada. rvosa@sfu.ca

Systematic Biology
|May 31, 2003
PubMed
Summary
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This study introduces a novel algorithm to accelerate phylogenetic tree exploration. It efficiently navigates complex likelihood landscapes, finding optimal evolutionary trees faster than traditional methods.

Area of Science:

  • Computational Biology
  • Phylogenetics
  • Bioinformatics

Background:

  • Phylogenetic tree reconstruction often faces challenges due to multiple likelihood maxima, requiring extensive exploration of tree space.
  • Existing stepwise addition methods are computationally constrained, limiting thorough exploration within practical timeframes.

Purpose of the Study:

  • To develop and present an algorithm that significantly enhances the speed of exploring the likelihood landscape in phylogenetic inference.
  • To combine the computational efficiency of distance-based methods with the accuracy of branch-swapping techniques for improved tree searching.

Main Methods:

  • An iterative algorithm is proposed that integrates distance-based tree construction for initial approximations and optimality criterion-based branch-swapping for refinement.

Related Experiment Videos

  • The algorithm perturbs the tree landscape by reweighting randomly sampled subsets of sequence data to move between local optima.
  • The approach leverages standard software for phylogenetic inference, ensuring broad applicability.

    • Main Results:

    • Tests on simulated and real datasets show that the algorithm finds optimal solutions faster than traditional stepwise addition heuristic searches.
    • The algorithm successfully identified known 'tree islands' in a published dataset more rapidly than stepwise addition methods.
    • Demonstrated improved efficiency in exploring complex phylogenetic likelihood landscapes.

    Conclusions:

    • The presented algorithm offers a computationally efficient solution for exploring complex phylogenetic tree spaces.
    • It accelerates the discovery of optimal phylogenetic trees, including in the presence of challenging 'tree islands'.
    • The method is readily implementable with existing phylogenetic inference software.