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Related Experiment Videos

Estimation of evolutionary parameters with phylogenetic trees.

Qiang Wang1, Laura A Salter, Dennis K Pearl

  • 1Department of Statistics, The Ohio State University, Columbus, OH 43210, USA.

Journal of Molecular Evolution
|December 18, 2002
PubMed
Summary

This study introduces a new Monte Carlo sampling method for estimating evolutionary model parameters in phylogenetic analysis. This approach improves variance estimation for nucleotide sequence data, enhancing phylogenetic tree accuracy.

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Accurate phylogenetic analysis of nucleotide sequences relies heavily on the chosen evolutionary model.
  • Maximum likelihood (ML) methods are widely used but require robust parameter estimation and variance assessment.

Purpose of the Study:

  • To develop and evaluate novel methods for simultaneously estimating phylogenetic tree topology and evolutionary model parameters.
  • To improve the estimation of standard errors for these parameters, crucial for assessing phylogenetic uncertainty.

Main Methods:

  • Proposed a new conditional variance estimate using Monte Carlo sampling (MCS) to approximate expected information.
  • Compared the MCS-based variance estimate with the traditional observed information method via simulations.

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  • Developed a bootstrapping approach combined with MCS for estimating unconditional standard errors when tree topology and parameters are estimated simultaneously.

    • Main Results:

    • The MCS method provides a reliable estimate of conditional variance for evolutionary model parameters.
    • Simulations demonstrated the performance of the MCS method under various conditions.
    • The bootstrapping and MCS combination effectively estimates unconditional standard errors.

    Conclusions:

    • The proposed MCS method enhances variance estimation in phylogenetic analysis.
    • This approach offers a robust way to assess uncertainty in evolutionary model parameters and tree topology.
    • The methods were successfully applied to a real dataset of papillomavirus sequences, demonstrating practical utility.