TY - JOUR
T1 - Phylogenetic inference using RevBayes
AU - Höhna, Sebastian
AU - Landis, Michael J.
AU - Heath, Tracy A.
N1 - Publisher Copyright:
© 2017 by John Wiley & Sons, Inc.
PY - 2017/3/1
Y1 - 2017/3/1
N2 - 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 probabilisticgraphical 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.
AB - 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 probabilisticgraphical 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.
KW - Bayesian phylogenetics
KW - Markov chain monte carlo
KW - posterior probabilities
KW - probabilistic graphical models
KW - substitution model
UR - http://www.scopus.com/inward/record.url?scp=85029438195&partnerID=8YFLogxK
U2 - 10.1002/cpbi.22
DO - 10.1002/cpbi.22
M3 - Article
C2 - 28463399
AN - SCOPUS:85029438195
SN - 1934-3396
VL - 2017
SP - 6.16.1-6.16.34
JO - Current Protocols in Bioinformatics
JF - Current Protocols in Bioinformatics
ER -