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  BEAST 2.5: an advanced software platform for Bayesian evolutionary analysis

Bouckaert, R., Vaughan, T. G., Barido-Sottani, J., Duchêne, S., Fourment, M., Gavryushkina, A., et al. (2019). BEAST 2.5: an advanced software platform for Bayesian evolutionary analysis. PLoS Computational Biology, 15(4): e1006650. doi:10.1371/journal.pcbi.1006650.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-9F7C-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-A276-1
Genre: Journal Article

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 Creators:
Bouckaert, Remco1, Author              
Vaughan, Timothy G., Author
Barido-Sottani, Joelle, Author
Duchêne, Sebastián, Author
Fourment, Mathieu, Author
Gavryushkina, Alexandra, Author
Heled, Joseph, Author
Jones, Graham, Author
Kühnert, Denise2, Author              
De Maio, Nicola, Author
Matschiner, Michael, Author
Mendes, Fábio K., Author
Müller, Nicola F., Author
Ogilvie, Huw A., Author
du Plessis, Louis, Author
Popinga, Alex, Author
Rambaut, Andrew, Author
Rasmussen, David, Author
Siveroni, Igor, Author
Suchard, Marc A., Author
Wu, Chieh-Hsi, AuthorXie, Dong, AuthorZhang, Chi, AuthorStadler, Tanja, AuthorDrummond, Alexei J., Author more..
Affiliations:
1Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Max Planck Society, ou_2074311              
2tide, Max Planck Institute for the Science of Human History, Max Planck Society, ou_2591691              

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 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.

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Language(s): eng - English
 Dates: 2019-04-08
 Publication Status: Published online
 Pages: 28
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1006650
Other: shh2223
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 15 (4) Sequence Number: e1006650 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1