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Divergence time and evolutionary rate estimation with multilocus data.

Jeffrey L Thorne1, Hirohisa Kishino

  • 1Bioinformatics Research Center, Box 7566, North Carolina State University, Raleigh, North Carolina 27695-7566, USA. thorne@statgen.nscu.edu

Systematic Biology
|October 25, 2002
PubMed
Summary
This summary is machine-generated.

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Bayesian methods now estimate evolutionary divergence times using multigene data. This approach helps detect correlated evolutionary rate changes and highlights the importance of fossil data for accurate dating.

Area of Science:

  • Evolutionary biology
  • Computational phylogenetics
  • Molecular evolution

Background:

  • Estimating evolutionary divergence times is crucial for understanding life's history.
  • Traditional methods often rely on single genes, which can be limiting.
  • Correlated changes in evolutionary rates among genes can impact divergence time estimates.

Purpose of the Study:

  • To extend Bayesian methods for estimating evolutionary divergence times to multigene datasets.
  • To develop a technique for detecting correlated evolutionary rate changes across genes.
  • To assess the impact of multigene data on divergence time estimation accuracy.

Main Methods:

  • Application of Bayesian phylogenetic inference to multigene sequence data.
  • Development and implementation of a method to detect rate correlation among genes.

Related Experiment Videos

  • Utilizing simulations to evaluate the performance of the extended methodology.
  • Illustration with a real-world dataset from diverse plant taxa.

    • Main Results:

    • Bayesian divergence time estimation is improved with multigene datasets.
    • A novel technique effectively identifies correlated evolutionary rate changes.
    • Simulations demonstrate the benefits of incorporating multiple genes.
    • The study emphasizes the confounding of evolutionary rates and times in sequence data.

    Conclusions:

    • Multigene data enhance the accuracy of Bayesian divergence time estimation.
    • Detecting correlated rate changes is essential for robust phylogenetic analyses.
    • Fossil information is critical for resolving the rate-time confounding issue.
    • This methodology provides a more comprehensive framework for evolutionary studies.