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Dating Microbial Evolution with MCMCtree.

Mario Dos Reis1

  • 1School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK. m.dosreisbarros@qmul.ac.uk.

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Summary
This summary is machine-generated.

MCMCtree accelerates divergence time estimation in microbial phylogenies using a novel likelihood approximation. This method enables analysis of large datasets, aiding evolutionary rate and divergence time studies.

Keywords:
Bayesian methodEukaryoteFossil recordInfluenzaLikelihood approximationMolecular clockPhylogenyProkaryote

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Phylogenetics

Background:

  • Estimating divergence times in microbial phylogenies is crucial for understanding evolutionary history.
  • Traditional methods can be computationally intensive, limiting the analysis of large datasets.

Purpose of the Study:

  • To present a protocol for using MCMCtree to estimate divergence times in microbial phylogenies.
  • To highlight MCMCtree's computational efficiency for large-scale phylogenetic analyses.

Main Methods:

  • Utilizing MCMCtree with a likelihood approximation for accelerated Bayesian Markov Chain Monte Carlo (MCMC) sampling.
  • Applying MCMCtree to microbial eukaryote/prokaryote and influenza virus phylogenies for Bayesian clock dating.

Main Results:

  • The likelihood approximation in MCMCtree significantly speeds up computation for divergence time and evolutionary rate estimation.
  • MCMCtree effectively analyzes large phylogenies with numerous taxa and extensive molecular alignments.

Conclusions:

  • MCMCtree provides an efficient and powerful tool for divergence time estimation in microbial phylogenetics.
  • The protocol offers practical guidance for constructing priors and assessing MCMC convergence, even without fossil or sampling time data.