Taming the BEAST—A Community Teaching Material Resource for BEAST 2

Abstract

Phylogenetics and phylodynamics are central topics in modern evolutionary biology. Phylogenetic methods reconstruct the evolutionary relationships among organisms, whereas phylodynamic approaches reveal the underlying diversification processes that lead to the observed relationships. These two fields have many practical applications in disciplines as diverse as epidemiology, developmental biology, palaeontology, ecology, and linguistics. The combination of increasingly large genetic data sets and increases in computing power is facilitating the development of more sophisticated phylogenetic and phylodynamic methods. Big data sets allow us to answer complex questions. However, since the required analyses are highly specific to the particular data set and question, a black-box method is not sufficient anymore. Instead, biologists are required to be actively involved with modeling decisions during data analysis. The modular design of the Bayesian phylogenetic software package BEAST 2 enables, and in fact enforces, this involvement. At the same time, the modular design enables computational biology groups to develop new methods at a rapid rate. A thorough understanding of the models and algorithms used by inference software is a critical prerequisite for successful hypothesis formulation and assessment. In particular, there is a need for more readily available resources aimed at helping interested scientists equip themselves with the skills to confidently use cutting-edge phylogenetic analysis software. These resources will also benefit researchers who do not have access to similar courses or training at their home institutions. Here, we introduce the “Taming the Beast” (https://taming-the-beast.github.io/) resource, which was developed as part of a workshop series bearing the same name, to facilitate the usage of the Bayesian phylogenetic software package BEAST 2.

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

“…mrbayes, beast and beast2 enable the co-estimation of the phylogeny and divergence times and allow for the implementation of both node-dating and tip-dating approaches, including total-evidence dating (where morphological data can be used for fossil placement). Comprehensive and user-friendly tutorials are available for mrbayes (Zhang, 2017), mcmctree (https://github.com/ mariodosreis/divtime) and beast2 (Barido-Sottani et al, 2017). Divergence time estimation using penalized-likelihood include 'r8s' (Sanderson, 2003), 'treePL' (Smith & O'Meara, 2012) and the 'chronos' function of the R package ape (Paradis, 2004;Paradis, 2013).…”

BEAST 2.5: An Advanced Software Platform for Bayesian Evolutionary Analysis

“…can be found on http://BEAST2.org/. Additionally, 655 tutorials for many of the described packages can be found as part of the 656 http://taming-the-beast.org/ platform [116]. At Google groups, there is a forum 657 (https://groups.google.com/forum/#!forum/beast-users) for users to discuss 658 questions.…”

“…Since the first release of BEAST 2 there has been a large expansion of core features, an 668 increase in the number of developers, and a large increase in the number of models and 669 the number of packages available. There has also been the publication of a book [2] and 670 the introduction of a regular series of week-long in-depth Taming the BEAST 671 workshops [116]. The BEAST 2 community has rapidly grown over the past 5 years and 672 the software has grown (with respect to other similar software packages) in a number of 673 distinct directions: (i) hierarchical multi-species coalescent models for species tree 674 estimation, (ii) fossilized birth-death models for macroevolution and total-evidence RevBayes [118].…”

“…112 In addition, some of us (TGV, TS) are currently working on including time tree 113 polytomies in BEAST 2, as may be relevant to, for example, super-spreading events in 114 infectious disease. 115 New models 116 A Bayesian phylodynamic analysis requires the specification of a model for 117 substitutions, a clock model, and a population dynamic model generating the 118 phylogenetic structure, whether that be a tree, a phylogenetic network or a hierarchical 119 combination of the two. These models induce probability distributions for the proposed 120 can be constructed, and all pairs of site models can be compared for relative support a 146 posteriori.…”

Predicting the short-term success of human influenza A variants with machine learning

“…Trees contain both branch lengths and information in the form of the tree topology or shape. Phylogenetic trees have been used in infectious disease to estimate the basic reproduction number [45], parameters of transmission models [50], aspects of underlying contact networks [38,22,39,28] and in densely sampled datasets even person-to-person transmission events and timing [12,18,2,51]. It is therefore natural to hypothesize that phylogenetic tree structures and branching patterns contain information about short-term growth and fitness.…”