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Upper bounds on maximum likelihood for phylogenetic trees.

Michael D Hendy1, Barbara R Holland

  • 1Allan Wilson Centre for Molecular Ecology and Evolution, Massey University, Palmerston North, New Zealand. m.hendy@massey.ac.nz

Bioinformatics (Oxford, England)
|October 10, 2003
PubMed
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This study introduces analytical bounds to improve maximum likelihood phylogenetic tree searches. These bounds significantly reduce the computational search space, accelerating the identification of the most likely evolutionary tree.

Area of Science:

  • Computational Biology
  • Phylogenetics
  • Evolutionary Biology

Background:

  • Maximum likelihood estimation is crucial for phylogenetic tree inference.
  • Current computational methods face challenges with large datasets and complex tree spaces.
  • Analytical bounds are needed to guarantee and improve the efficiency of these searches.

Purpose of the Study:

  • To develop a mechanism for analytically deriving upper bounds on maximum likelihood for genetic sequence data.
  • To improve the efficiency and reliability of phylogenetic tree searches.
  • To facilitate the development of branch and bound search strategies.

Main Methods:

  • Introduction of a simple 'partition' bound for general models.
  • Development of tighter bounds for the two-state symmetric model under a molecular clock.

Related Experiment Videos

  • Simulation study using approximately 10^6 datasets on clock-like trees with five leaves.

    • Main Results:

    • The derived bounds can eliminate large proportions of the tree space in maximum likelihood searches.
    • Simulation results show elimination of 92% to 98% of trees.
    • This translates to a computational speed-up factor of 12 to 44.

    Conclusions:

    • The developed analytical bounds significantly enhance the efficiency of maximum likelihood phylogenetic tree searches.
    • These bounds provide a reliable method to prune the search space, overcoming limitations of current numerical computations.
    • The findings are vital for developing robust branch and bound algorithms in phylogenetics.