RevBayes: Bayesian phylogenetic inference using probabilistic graphical models and an interpreted language
During my PhD studies I joined John Huelsenbeck and Fredrik Ronquist to work on the successor software of MrBayes. Now I’m the leading developer within a great group of developers. 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.
RevBayes is the successor of MrBayes; however, these programs do not share a single line of code. RevBayes is entirely based on probabilistic graphical models, a powerful generic framework for specifying and analyzing statistical models. Phylogenetic graphical models can be specified interactively in RevBayes, piece by piece, using a new succinct and intuitive language called Rev. The primary strength of RevBayes is the simplicity to design, specify and implement new and complex (comparative) phylogenetic models. The graphical-model framework also provides pedagogical advantages: it removes the typical black box so that users can visualize the model they are specifying. This transparency will improve the understanding of phylogenetic models in our field, and will motivate the search for improvements to existing methods by brazenly exposing the model choices that we make to critical scrutiny. Check out the website and tutorials about RevBayes!
TESS: Bayesian inference of lineage diversification rates
Many fundamental questions in evolutionary biology entail estimating rates of lineage diversification (speciation – extinction) that are modeled using birth-death branching processes. Some of my earlier work (during my Ph.D. studies in Stockholm and my PostDoc in Davis) focused on estimating diversification rates and patterns, such as rate-shifts and mass-extinction events, from estimated molecular phylogenies (Höhna, 2013, Bioinformatics). I developed new theory for birth-death processes where rates are constant, vary continuously, or change episodically through time (Höhna, 2015, JTB). We then developed a flexible Bayesian framework and implement numerical methods to estimate parameters of these models from molecular phylogenies (May et al, 2016, MEE). Furthermore, different strategies to account for incomplete taxon sampling to improve parameter estimates (see Höhna et al., 2011, MBE and Höhna, 2014, PLoS one).
The models and methods have been implemented in the R package TESS (Höhna et al, 2016, Bioinformatics). TESS enables both statistical inference and efficient simulation under birth-death models. TESS also provides robust methods for comparing the relative and absolute fit of competing branching-process models to a given tree, thereby providing rigorous tests of biological hypotheses regarding patterns and processes of lineage diversification. There will be no further development of TESS because all new methods are implemented in RevBayes. TESS was only used while RevBayes was not ready at the time.