BEAST 2.5: An Advanced Software Platform for Bayesian Evolutionary Analysis

Abstract

Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and r 15, 2018 1/29 computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release. Author summaryBayesian phylogenetic inference methods have undergone considerable development in 1 recent years, and joint modelling of rich evolutionary data, including genomes, 2 phenotypes and fossil occurrences is increasingly common. Advanced computational 3 software packages that allow robust development of compatible (sub-)models which can 4 be composed into a full model hierarchy have played a key role in these developments. 5Developing scientific software is increasingly crucial to advancement in many fields of 6 biology. The challenges range from practical software development and engineering, 7 distributed team coordination, conceptual development and statistical modelling, to 8 validation and testing. BEAST 2 is one such computational software platform for 9 phylogenetics, population genetics and phylodynamics, and was first announced over 4 10 years ago. Here we describe the full range of new tools and models available on the 11 BEAST 2.5 platform, which expand joint evolutionary inference in many new directions, 12 especially for joint inference over multiple data types, non-tree models and complex 13 phylodynamics. 14 24 LogAnalyser, LogCombiner, TreeAnnotator, DensiTree [3], as well as a package 25 manager. 26 Shortly after its release, a number of packages were added, such as MASTER for 27 simulating stochastic population dynamics models [4], MultiTypeTree for inferring 28 structured coalescent models [5], RBS for reversible jump across substitution models [6], 29 SNAPP for multi species coalescent over SNP data [7], subst-bma for Bayesian model 30 averaging over site models [8], and BDSKY for the birth-death skyline tree model [9]. 31 All these package...

Host dispersal shapes the population structure of a tick‐borne bacterial pathogen

“…This is also what we observed on the ancestry recombination graph reconstructed using sequences of the 85 European STs and generated in beast2 (Bouckaert et al, ; Figure ). This method was used because we suspected recombination between housekeeping loci.…”

“…An ancestry recombination graph for the 85 STs present in Europe was reconstructed with beast2 software v. 2.5 (Bouckaert et al, ) and package bacter v. 2.2 (Didelot, Lawson, Darling, & Falush, ; Vaughan et al, ) using sequences of the eight housekeeping genes. beast2 was run three times with a unique tree and substitution model for the eight loci but with a lognormal‐relaxed clock model for each locus.…”

Dispersal out of Wallacea spurs diversification of Pteropus flying foxes, the world’s largest bats (Mammalia: Chiroptera)

“…To minimize noise and shorten analysis time for species tree inference, a maximum likelihood (ML) consensus tree was first inferred using IQ‐TREE 1.6.10 (Nguyen, Schmidt, von Haeseler, & Minh, ) on a dataset from which the uninformative loci BDNF, PLCB4, RAG‐1 and RAG‐2 were removed. A species tree was then estimated with BEAST 2.5.2 (Bouckaert et al, ) using a log‐normal relaxed clock birth–death model and the consensus ML tree as a starting guide tree. A Yule prior was not a better fit to our data, thus we chose the birth model, which is better suited as a null hypothesis for species diversification.…”

“…Markov chain performance for species trees was verified using Tracer to ensure ESS values above 200, appropriate burn‐in and chain convergence. This required using LogCombiner 2.5.2 (Bouckaert et al, ) to combine multiple BEAST runs with the same parameters with 20% burn‐in; the final sample of trees was run for 200 million generations. There were 14,401 trees in the final posterior distribution from BEAST .…”

Mitogenomic phylogenetic analyses of Leptogorgia virgulata and Leptogorgia hebes (Anthozoa: Octocorallia) from the Gulf of Mexico provides insight on Gorgoniidae divergence between Pacific and Atlantic lineages

“…Divergence time estimates were performed by Bayesian analyses using full mitochondrial genomes only, with the software BEAST 2.3.2 (Bouckaert et al, ). The alignment was partitioned as specified above for the Bayesian phylogenetic reconstruction (Table ).…”