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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
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Quantifying MCMC exploration of phylogenetic tree space.

Chris Whidden1, Frederick A Matsen2

  • 1Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA cwhidden@fhcrc.org.

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|January 30, 2015
PubMed
Summary

Understanding phylogenetic Markov chain Monte Carlo (MCMC) effectiveness requires assessing convergence speed. This study uses the subtree prune-and-regraft (SPR) metric to analyze phylogenetic tree topologies, revealing insights into MCMC performance and Bayesian phylogenetic posteriors.

Keywords:
Markov chain Monte Carlophylogenetic methodssubtree prune-and-regrafttopological peakstree space

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Statistical Modeling

Background:

  • Assessing the convergence of Markov chain Monte Carlo (MCMC) to posterior distributions is crucial for understanding phylogenetic analysis effectiveness.
  • The subtree prune-and-regraft (SPR) metric is well-suited for evaluating distances between phylogenetic tree topologies.
  • Standard MCMC methods are widely used in Bayesian phylogenetics but their efficiency can be affected by complex posterior landscapes.

Purpose of the Study:

  • To investigate the convergence rate of phylogenetic MCMC algorithms using the SPR metric.
  • To analyze the structure of phylogenetic tree posteriors and identify the presence of topological peaks.
  • To evaluate the efficiency of Metropolis-coupled MCMC (MCMCMC) and the reliability of conditional clade distribution (CCD) in multi-peak scenarios.

Main Methods:

  • Development of a novel graph-based approach for analyzing phylogenetic tree posteriors.
  • Application of the subtree prune-and-regraft (SPR) metric to quantify topological distances.
  • Investigation of Bayesian phylogenetic posteriors from real data sets using standard MCMC sampling.
  • Analysis of Metropolis-coupled MCMC (MCMCMC) efficiency in navigating posterior distributions.

Main Results:

  • The SPR metric provides a more informative measure of topological distances compared to simpler metrics unrelated to MCMC moves.
  • Topological peaks are confirmed to exist in Bayesian phylogenetic posteriors from real datasets sampled with standard MCMC.
  • The efficiency of MCMCMC in traversing valleys between posterior peaks was investigated.
  • Conditional clade distribution (CCD) was found to exhibit systematic issues when multiple peaks are present in the posterior.

Conclusions:

  • The SPR metric is a valuable tool for assessing MCMC convergence in phylogenetic analyses.
  • The presence of multiple peaks in phylogenetic posteriors necessitates careful consideration of MCMC sampling strategies.
  • Standard MCMC methods may require adjustments or alternative approaches to reliably explore complex posterior landscapes and avoid biased inferences, particularly when using metrics like CCD.