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Adaptive MCMC in Bayesian phylogenetics: an application to analyzing partitioned data in BEAST.

Guy Baele1, Philippe Lemey1, Andrew Rambaut2,3

  • 1Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.

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Summary

This study introduces a parallel Markov chain Monte Carlo (MCMC) method for phylogenetic analysis, improving computational efficiency in estimating evolutionary parameters from partitioned sequence data.

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Advances in sequencing generate large, partitioned molecular datasets requiring complex evolutionary models.
  • Estimating numerous evolutionary parameters for partitioned data increases computational burden.
  • Multi-core processors offer parallel computation potential not fully utilized in phylogenetic software.

Purpose of the Study:

  • To develop and implement a parallel Markov chain Monte Carlo (MCMC) approach for efficient estimation of multipartite parameters in phylogenetic analyses.
  • To leverage multi-core processing capabilities to address the computational challenges of partitioned evolutionary models.

Main Methods:

  • Proposed a Markov chain Monte Carlo (MCMC) approach utilizing an adaptive multivariate transition kernel.
  • Implemented the method within the BEAST software package for Bayesian phylogenetic inference.
  • Exploited multi-core processing for parallel estimation of parameters across partitioned sequence data.

Main Results:

  • Demonstrated improved efficiency in estimating multipartite parameters compared to standard univariate approaches.
  • Achieved significant gains in MCMC integration efficiency, exceeding 14-fold in specific cases (e.g., non-coding partition relative rate parameter).
  • Successfully applied the parallel approach to real-world phylogenetic datasets.

Conclusions:

  • The proposed parallel MCMC approach enhances the efficiency of phylogenetic analyses with partitioned data.
  • This method effectively utilizes multi-core processors, reducing computational burden.
  • The implementation in BEAST provides a practical tool for researchers in evolutionary biology and bioinformatics.