BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis

Bouckaert, Remco; Vaughan, Timothy G.; Barido‐Sottani, Joëlle; Duchêne, Sebastián; Fourment, Mathieu; Gavryushkina, Alexandra; Heled, Joseph; Jones, Graham; Kühnert, Denise; Maio, Nicola De; Matschiner, Michael; Mendes, Fábio K.; Müller, Nicola F.; Ogilvie, Huw A.; Plessis, Louis du; Popinga, Alex; Rambaut, Andrew; Rasmussen, David A.; Siveroni, Igor; Suchard, Marc A.; Wu, Chieh‐Hsi; Xie, Dong; Zhang, Chi; Stadler, Tanja; Drummond, Alexei J.

doi:10.1371/journal.pcbi.1006650

articlePLoS Computational BiologyApr 8, 2019GOLD OA

BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis

RBRemco Bouckaert TGTimothy G. Vaughan JBJoëlle Barido‐Sottani SDSebastián Duchêne MFMathieu Fourment

University of Auckland · Max Planck Institute for the Science of Human History · +16 more institutions

PubMed

Indexed incrossrefdatacitedoajpubmed

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…

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Keywords

Computer science
Software
Data science
Hierarchy
Bayesian probability
Inference
Software development
Pace

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