BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis
BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis
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.
Bouckaert, Remco
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Vaughan, Timothy G.
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Barido-Sottani, Joelle
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Duchêne, Sebastián
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Fourment, Mathieu
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Gavryushkina, Alexandra
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Heled, Joseph
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Jones, Graham
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Kühnert, Denise
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De Maio, Nicola
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Wu, Chieh-Hsi
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8 April 2019
Bouckaert, Remco
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Vaughan, Timothy G.
46e6b9a3-2151-4495-ae43-f266ba77af5f
Barido-Sottani, Joelle
957e40d3-c7f4-49b5-905c-5fdb88becef2
Duchêne, Sebastián
2fdf868c-96e5-416d-8b9f-e2108346d451
Fourment, Mathieu
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Gavryushkina, Alexandra
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Heled, Joseph
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Jones, Graham
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Kühnert, Denise
ec21eb4a-ba28-4a35-8f74-04ec1edb7552
De Maio, Nicola
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Wu, Chieh-Hsi
ace630c6-2095-4ade-b657-241692f6b4d3
Bouckaert, Remco, Vaughan, Timothy G., Barido-Sottani, Joelle, Duchêne, Sebastián, Fourment, Mathieu, Gavryushkina, Alexandra, Heled, Joseph, Jones, Graham, Kühnert, Denise, De Maio, Nicola and Wu, Chieh-Hsi
(2019)
BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis.
PLoS Computational Biology, 15 (4), [e1006650].
(doi:10.1371/journal.pcbi.1006650).
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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Accepted/In Press date: 4 February 2019
Published date: 8 April 2019
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Local EPrints ID: 437893
URI: http://eprints.soton.ac.uk/id/eprint/437893
ISSN: 1553-734X
PURE UUID: 2b7cfed8-40c4-493e-a5e7-328813f5987c
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Date deposited: 21 Feb 2020 17:31
Last modified: 17 Mar 2024 04:00
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Contributors
Author:
Remco Bouckaert
Author:
Timothy G. Vaughan
Author:
Joelle Barido-Sottani
Author:
Sebastián Duchêne
Author:
Mathieu Fourment
Author:
Alexandra Gavryushkina
Author:
Joseph Heled
Author:
Graham Jones
Author:
Denise Kühnert
Author:
Nicola De Maio
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