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Maximum-likelihood methods for phylogeny estimation.

Jack Sullivan1

  • 1Department of Biological Sciences, The Initiative for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho 83844-3051, USA.

Methods in Enzymology
|May 4, 2005
PubMed
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Maximum-likelihood (ML) estimation in phylogenetics is sophisticated due to algorithmic and computational advances. This study demonstrates ML model selection and hypothesis testing for robust phylogenetic inference.

Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Maximum-likelihood (ML) estimation in phylogenetics has advanced significantly.
  • Algorithmic improvements, better evolutionary models, and cluster computing have enhanced ML methods.

Purpose of the Study:

  • To provide a basic understanding of ML estimation in phylogenetics.
  • To demonstrate ML model selection using hierarchical likelihood-ratio tests.
  • To illustrate statistical testing of phylogenetic hypotheses and model adequacy.

Main Methods:

  • Application of the general principle of ML estimation.
  • Dynamic approach to hierarchical likelihood-ratio tests for model selection.
  • Utilizing PAUP* software for switching between optimality criteria (ML, parsimony, minimum evolution).

Related Experiment Videos

  • Parametric bootstrap tests for hypothesis and model evaluation.

    • Main Results:

    • PAUP* facilitates easy switching between different phylogenetic optimality criteria.
    • Parametric bootstrap tests provide absolute statistical assessment of phylogenetic hypotheses and model fit.
    • Advanced ML methods are becoming feasible with parallelized computation.

    Conclusions:

    • Sophisticated ML estimation is a powerful tool for phylogenetic inference.
    • Hierarchical likelihood-ratio tests and parametric bootstrapping offer robust methods for model selection and hypothesis testing.
    • The increasing availability of computational power supports advanced phylogenetic analyses.