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 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.

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, 2019;Figure 5 Ixodes ricinus is a generalist tick and birds are known to be important hosts for its immature stages (Norte et al, 2012;Santos-Silva et al, 2011). The other tick species (I. frontalis, I. arboricola and I. ventalloi) and genera (Haemaphysalis and Hyalomma) collected from birds in this study have also been previously reported on birds (Diakou et al, 2016;Norte et al, 2012;Pérez-Eid, 2007) and differ in vector competence for Borrelia (Eisen & Lane, 2002;.…”

Beyond R0: Heterogeneity in secondary infections and probabilistic epidemic forecasting

“…Both for generating accurate predictions of epidemic risk and controlling the outbreak, it is vital to begin contact tracing before numerous transmission chains become widely distributed across space [38,39] Second, viral genome sequences provide information on both the timing of the outbreak [40][41][42] and structure of secondary cases [43][44][45]. For example, methods exist to reconstruct transmission trees for sampled sequences using simple mutational models to construct a likelihood for a specific transmission tree [46,47] and translate coalescent rates into key epidemiological parameters [48][49][50]. Despite the potential for genome sequencing to revolutionize outbreak response, the global public health community still struggles to coordinate data sharing across international borders, between academic researchers, and with private companies [51][52][53].…”

Phylogenomics — principles, opportunities and pitfalls of big‐data phylogenetics

“…The most well-known BI software includes mrbayes (Ronquist et al, 2012b), exabayes (Aberer et al, 2014), phylobayes (Lartillot et al, 2013), revbayes , beast (Suchard et al, 2018), and beast2 (Bouckaert et al, 2019). mrbayes is a well-established program, with very helpful documentation and many online tutorials available.…”