Evolutionary Biology

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Software for Phylogenetic Inference

Recently, we introduced the concept of probabilistic graphical models to phylogenetics (Höhna et al., 2014, Systematic Biology). This model representation has the advantage of being easily extendable to more complex (i.e., realistic) models. We combined and implemented this mathematical and statistical theory in a new computer program called RevBayes (Höhna et al., 2016, Systematic Biology). RevBayes is a general and flexible software mostly intended for Bayesian inference of phylogeny. Programs for Bayesian inference of phylogeny currently implement a unique and fixed suite of models. Consequently, users of these software packages are simultaneously forced to use a number of programs for a given study, while also lacking the freedom to explore models that have not been deemed interesting by the developers of those programs. RevBayes seeks to address these problems. The features of RevBayes include unrooted and rooted phylogeny inference, divergence time estimation, diversification rate estimation, historical biogeography, and discrete and continuous trait evolution.

Development in RevBayes is ongoing and extremely active. Specifically, we use RevBayes as a framework to implement and test new statistical models as well as new algorithms and methods for more efficient inference and hypothesis testing.

• S Höhna, MJ Landis and TA Heath: Phylogenetic Inference using RevBayes. Current Protocols in Bioinformatic, 2017, 57:6.16.1-6.16.34, link
• S Höhna, MJ Landis, TA Heath, B Boussau, BR Moore, N Lartillot, JP Huelsenbeck and F Ronquist: RevBayes: Bayesian Phylogenetic Inference Using Graphical Models and an Interactive Model-Specification Language, Systematic Biology, 2016, 65 (4), 726-736, link
• S Höhna, TA Heath, B Boussau, MJ Landis, F Ronquist and JP Huelsenbeck: Probabilistic Graphical Model Representation in Phylogenetics. Systematic Biology, 2014, 63, 753-771, link
• S Höhna and AJ Drummond: Guided tree topology proposals for Bayesian phylogenetic inference. Systematic Biology, 2012, 61, 1-11, link