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BEAST 2: A Software Platform for Bayesian Evolutionary Analysis
Open accessAbstract
We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as the BEAST 1 software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format.
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“The model implemented in BEAST2 allows transitions between conserved and accelerated rates in both directions.”
From Bayesian Detection of Convergent Rate Changes of Conserved Noncoding Elements on Phylogenetic Trees (2019) · cites this paper as Bouckaert et al. 2014
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“As seen in figure 3, the accuracy of both methods increases as the ratio between accelerated and conserved rates increases”
From Bayesian Detection of Convergent Rate Changes of Conserved Noncoding Elements on Phylogenetic Trees (2019) · cites this paper as Bouckaert et al. 2014
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Metadata
- Authors
- Bouckaert, R., Heled, J., Kühnert, D., Vaughan, T., Wu, C.-H., Xie, D., Suchard, M. A., Rambaut, A., & Drummond, A. J.
- Affiliations
- University of Auckland (NZ)
- University of California, Los Angeles (US)
- University of Edinburgh (GB)
- Corresponding author
- Bouckaert, Remco
- Drummond, Alexei J.
- ORCID
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-04-10
- Volume
- 10
- Issue
- 4
- Pages
- e1003537
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3985171
- OpenAlex
- W2026062398
- Cited by
- 6,527
- References
- 31
- Citations by year
- 2026: 364 · 2025: 464 · 2024: 509 · 2023: 492 · 2022: 588 · 2021: 745 · 2020: 801 · 2019: 784 · 2018: 724 · 2017: 484 · 2016: 302 · 2015: 213 · 2014: 44
- FWCI
- 131.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (publishedVersion, cc-by)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- ePrints Soton (University of Southampton) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- ResearchSpace (University of Auckland) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Funders
- National Science Foundation (1264153, 0856099)
- National Evolutionary Synthesis Center
- Wellcome Trust (095831)
- Royal Society
- Royal Society Te Apārangi
- National Institutes of Health
- National Human Genome Research Institute (R01HG006139)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- BEAST2, Bayesian phylogenetics, Markov chain Monte Carlo, package management, extensibility, phylodynamics, model averaging, substitution models, XML file format, open source software
- MeSH terms
- Bayes Theorem, Biological Evolution, Programming Languages, Software
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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