BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis
Bouckaert, R
Vaughan, TG
Barido-Sottani, J
Duchêne, S
Fourment, M
Gavryushkina, A
Heled, J
Jones, G
Kühnert, D
De Maio, N
Matschiner, M
Mendes, FK
Müller, NF
Ogilvie, HA
Du Plessis, L
Popinga, A
Rambaut, A
Rasmussen, D
Siveroni, I
Suchard, MA
Wu, CH
Xie, D
Zhang, C
Stadler, T
Drummond, AJ
- Publication Type:
- Journal Article
- Citation:
- PLoS Computational Biology, 2019, 15 (4)
- Issue Date:
- 2019-01-01
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Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Bouckaert, R | en_US |
dc.contributor.author | Vaughan, TG | en_US |
dc.contributor.author | Barido-Sottani, J | en_US |
dc.contributor.author | Duchêne, S | en_US |
dc.contributor.author |
Fourment, M |
en_US |
dc.contributor.author | Gavryushkina, A | en_US |
dc.contributor.author | Heled, J | en_US |
dc.contributor.author | Jones, G | en_US |
dc.contributor.author | Kühnert, D | en_US |
dc.contributor.author | De Maio, N | en_US |
dc.contributor.author | Matschiner, M | en_US |
dc.contributor.author | Mendes, FK | en_US |
dc.contributor.author | Müller, NF | en_US |
dc.contributor.author | Ogilvie, HA | en_US |
dc.contributor.author | Du Plessis, L | en_US |
dc.contributor.author | Popinga, A | en_US |
dc.contributor.author | Rambaut, A | en_US |
dc.contributor.author | Rasmussen, D | en_US |
dc.contributor.author | Siveroni, I | en_US |
dc.contributor.author | Suchard, MA | en_US |
dc.contributor.author | Wu, CH | en_US |
dc.contributor.author | Xie, D | en_US |
dc.contributor.author | Zhang, C | en_US |
dc.contributor.author | Stadler, T | en_US |
dc.contributor.author | Drummond, AJ | en_US |
dc.date.available | 2019-02-04 | en_US |
dc.date.issued | 2019-01-01 | en_US |
dc.identifier.citation | PLoS Computational Biology, 2019, 15 (4) | en_US |
dc.identifier.issn | 1553-734X | en_US |
dc.identifier.uri | http://hdl.handle.net/10453/133818 | |
dc.description.abstract | © 2019 Bouckaert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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. | en_US |
dc.relation.ispartof | PLoS Computational Biology | en_US |
dc.relation.isbasedon | 10.1371/journal.pcbi.1006650 | en_US |
dc.subject.classification | Bioinformatics | en_US |
dc.subject.mesh | Animals | en_US |
dc.subject.mesh | Humans | en_US |
dc.subject.mesh | Monte Carlo Method | en_US |
dc.subject.mesh | Bayes Theorem | en_US |
dc.subject.mesh | Markov Chains | en_US |
dc.subject.mesh | Computational Biology | en_US |
dc.subject.mesh | Evolution, Molecular | en_US |
dc.subject.mesh | Phylogeny | en_US |
dc.subject.mesh | Models, Genetic | en_US |
dc.subject.mesh | Computer Simulation | en_US |
dc.subject.mesh | Software | en_US |
dc.subject.mesh | Biological Evolution | en_US |
dc.title | BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis | en_US |
dc.type | Journal Article | |
utslib.citation.volume | 4 | en_US |
utslib.citation.volume | 15 | en_US |
utslib.for | 0699 Other Biological Sciences | en_US |
utslib.for | 0803 Computer Software | en_US |
utslib.for | 01 Mathematical Sciences | en_US |
utslib.for | 06 Biological Sciences | en_US |
utslib.for | 08 Information and Computing Sciences | en_US |
pubs.embargo.period | Not known | en_US |
pubs.organisational-group | /University of Technology Sydney | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Science | |
pubs.organisational-group | /University of Technology Sydney/Strength - ithree - Institute of Infection, Immunity and Innovation | |
utslib.copyright.status | open_access | |
pubs.issue | 4 | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 15 | en_US |
Abstract:
© 2019 Bouckaert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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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