| Literature DB >> 28463399 |
Sebastian Höhna1,2, Michael J Landis3, Tracy A Heath4.
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
Mesh:
Year: 2017 PMID: 28463399 DOI: 10.1002/cpbi.22
Source DB: PubMed Journal: Curr Protoc Bioinformatics ISSN: 1934-3396