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UNIT 6.16 Phylogenetic Inference Using RevBayes

  1. Sebastian Höhna1,2,
  2. Michael J. Landis3,
  3. Tracy A. Heath4

Published Online: 2 MAY 2017

DOI: 10.1002/cpbi.22

Current Protocols in Bioinformatics

Current Protocols in Bioinformatics

How to Cite

Höhna, S., Landis, M.J. and Heath, T.A. 2017. Phylogenetic inference using RevBayes. Curr. Protoc. Bioinform. 57:6.16.1-6.16.34. doi: 10.1002/cpbi.22

Author Information

  1. 1

    Department of Integrative Biology, University of California, Berkeley, California

  2. 2

    Department of Statistics, University of California, Berkeley, California

  3. 3

    Department of Ecology & Evolutionary Biology, Yale University, New Haven, Connecticut

  4. 4

    Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, Iowa

Publication History

  1. Published Online: 2 MAY 2017

Abstract

Bayesian phylogenetic inference aims to estimate the evolutionary relationships among different lineages (species, populations, gene families, viral strains, etc.) in a model-based statistical framework that uses the likelihood function for parameter estimates. In recent years, evolutionary models for Bayesian analysis have grown in number and complexity. RevBayes uses a probabilistic-graphical model framework and an interactive scripting language for model specification to accommodate and exploit model diversity and complexity within a single software package. In this unit we describe how to specify standard phylogenetic models and perform Bayesian phylogenetic analyses in RevBayes. The protocols focus on the basic analysis of inferring a phylogeny from single and multiple loci, describe a hypothesis-testing approach, and point to advanced topics. Thus, this unit is a starting point to illustrate the power and potential of Bayesian inference under complex phylogenetic models in RevBayes. © 2017 by John Wiley & Sons, Inc.

Keywords:

  • Bayesian phylogenetics;
  • Markov chain Monte Carlo;
  • posterior probabilities;
  • probabilistic graphical models;
  • substitution model