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

• Published in: Systematic biology Vol. 67; no. 1; pp. 170 - 174
• Main Authors: Barido-Sottani, Joëlle, Bošková, Veronika, du Plessis, Louis, Kühnert, Denise, Magnus, Carsten, Mitov, Venelin, Müller, Nicola F., Pečerska, Jūlija, Rasmussen, David A., Zhang, Chi, Drummond, Alexei J., Heath, Tracy A., Pybus, Oliver G., Vaughan, Timothy G., Stadler, Tanja
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01-01-2018
• Subjects: Algorithms; Bayesian analysis; Biodiversity; Computational Biology - education; Computational Biology - methods; Computer applications; Computer programs; Data processing; Developmental biology; Epidemiology; Evolutionary biology; Mathematical models; Phylogenetics; Phylogeny; Software; Software for Systematics and Evolution; Teaching Materials; MCMC; phylogenetics; Bayesian inference; phylodynamics
• ISSN: 1063-5157; 1076-836X
• DOI: 10.1093/sysbio/syx060

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.