Literature DB >> 28463399

Phylogenetic Inference Using RevBayes.

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.
Copyright © 2017 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


  4 in total

Review 1.  A Systematist's Guide to Estimating Bayesian Phylogenies From Morphological Data.

Authors:  April M Wright
Journal:  Insect Syst Divers       Date:  2019-06-18

2.  The Occurrence Birth-Death Process for Combined-Evidence Analysis in Macroevolution and Epidemiology.

Authors:  Jérémy Andréoletti; Antoine Zwaans; Rachel C M Warnock; Gabriel Aguirre-Fernández; Joëlle Barido-Sottani; Ankit Gupta; Tanja Stadler; Marc Manceau
Journal:  Syst Biol       Date:  2022-10-12       Impact factor: 9.160

3.  Drift and Directional Selection Are the Evolutionary Forces Driving Gene Expression Divergence in Eye and Brain Tissue of Heliconius Butterflies.

Authors:  Ana Catalán; Adriana D Briscoe; Sebastian Höhna
Journal:  Genetics       Date:  2019-08-29       Impact factor: 4.562

4.  Locally adaptive Bayesian birth-death model successfully detects slow and rapid rate shifts.

Authors:  Andrew F Magee; Sebastian Höhna; Tetyana I Vasylyeva; Adam D Leaché; Vladimir N Minin
Journal:  PLoS Comput Biol       Date:  2020-10-28       Impact factor: 4.475

  4 in total

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