TY - JOUR
T1 - Phylogenetic inference using RevBayes
AU - Höhna, Sebastian
AU - Landis, Michael J.
AU - Heath, Tracy A.
N1 - Funding Information:
We thank William Pearson for the invita tion to write this protocol. RevBayes, the Rev language, and the software documentation are developed collaboratively by many contributors (https://github.com/revbayes/ revbayes/graphs/contributors). We especially thank the other members of the core development team: Bastien Boussau, John Huelsenbeck, Nicolas Lartillot, and Fredrik Ronquist. Additionally, we thank Brian Moore for contributions to RevBayes tutorials from which the presented protocols are derived. Generous funding from various sources supported this work: S.H. was funded by the Miller Institute for Basic Research in Science; M.J.L. was supported by the Yale Institute of Biospheric Studies under the Gaylord Donnel-ley Postdoctoral Environmental Fellowship; and T.A.H. was funded by research grants
Funding Information:
from the U.S. National Science Foundation (DEB-1556853 and DEB-1556615).
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 / editoral board, Andreas D. Baxevanis ... [et al.]
JF - Current protocols in bioinformatics / editoral board, Andreas D. Baxevanis ... [et al.]
ER -