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BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis
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Abstract
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. Author summary Bayesian phylogenetic inference methods have undergone considerable development in recent years, and joint modelling of rich evolutionary data, including genomes, phenotypes and fossil occurrences is increasingly common. 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 scientific software is increasingly crucial to advancement in many fields of biology. The challenges range from practical software development and engineering, distributed team coordination, conceptual development and statistical modelling, to validation and testing. BEAST 2 is one such computational software platform for phylogenetics, population genetics and phylodynamics, and was first announced over 4 years ago. Here we describe the full range of new tools and models available on the BEAST 2.5 platform, which expand joint evolutionary inference in many new directions, especially for joint inference over multiple data types, non-tree models and complex phylodynamics.
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“Methods for inferring these latent phylogenetic trees continue to be of substantial interest 92-94, but are frustrated by long run times and poor scalability to large datasets.”
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Pseudo Dollo models for the evolution of binary characters along a treePreprint
Bouckaert, R., & Robbeets, M. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/207571 · Cited by 54
Self-citationInfectious disease: Tough choices to reduce Ebola transmission
Whitty, C. W. M., Farrar, J., Ferguson, N. M., Edmunds, W. J., Piot, P., Leach, M., & Davies, S. C. (2014) · Nature · doi:10.1038/515192a · Cited by 53
Unifying Vertical and Nonvertical Evolution: A Stochastic ARG-based Framework
Bloomquist, E. W., & Suchard, M. A. (2009) · Systematic Biology · doi:10.1093/sysbio/syp076 · Cited by 50
Self-citationPhylogeography by diffusion on a sphere: whole world phylogeography
Bouckaert, R. (2016) · PeerJ · doi:10.7717/peerj.2406 · Cited by 50
Self-citationInferring Ancestral Recombination Graphs from Bacterial Genomic Data
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Self-citationBayesian Selection of Nucleotide Substitution Models and Their Site Assignments
Wu, C., Suchard, M. A., & Drummond, A. J. (2012) · Molecular Biology and Evolution · doi:10.1093/molbev/mss258 · Cited by 48
Self-citationBayesian inference of fine-scale recombination rates using population genomic data
Wang, Y., & Rannala, B. H. (2008) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2008.0172 · Cited by 46
Phylodynamic Model Adequacy Using Posterior Predictive Simulations
Duchêne, S., Bouckaert, R., Duchêne, D. A., Stadler, T., & Drummond, A. J. (2018) · Systematic Biology · doi:10.1093/sysbio/syy048 · Cited by 43
Self-citationDivergence Estimation in the Presence of Incomplete Lineage Sorting and Migration
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Self-citationMüller, N. F., Dudas, G., & Stadler, T. (2019) · Virus Evolution · doi:10.1093/ve/vez030 · Cited by 36
Self-citationP3: Phylogenetic Posterior Prediction in RevBayes
Höhna, S., Coghill, L. M., Mount, G. G., Thomson, R. C., & Brown, J. M. (2017) · Molecular Biology and Evolution · doi:10.1093/molbev/msx286 · Cited by 35
Popinga, A., Vaughan, T. G., Stadler, T., & Drummond, A. J. (2014) · Genetics · doi:10.1534/genetics.114.172791 · Cited by 30
Self-citationNovel Integrative Modeling of Molecules and Morphology across Evolutionary Timescales
Ogilvie, H. A., Mendes, F. K., Vaughan, T. G., Matzke, N. J., Stadler, T., Welch, D., & Drummond, A. J. (2021) · Systematic Biology · doi:10.1093/sysbio/syab054 · Cited by 27
Self-citationLocal and relaxed clocks: the best of both worlds
Fourment, M., & Darling, A. E. (2018) · PeerJ · doi:10.7717/peerj.5140 · Cited by 22
Self-citationJoint inference of species histories and gene flowPreprint
Müller, N. F., Ogilvie, H. A., Zhang, C., Fontaine, M. C., Amaya-Romero, J.-E., Drummond, A. J., & Stadler, T. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/348391 · Cited by 18
Self-citationNovel Integrative Modeling of Molecules and Morphology across Evolutionary TimescalesPreprint
Ogilvie, H., Mendes, F. K., Vaughan, T. G., Matzke, N. J., Stadler, T., Welch, D., & Drummond, A. J. (2018) · ResearchSpace (University of Auckland) · doi:10.1101/242875 · Cited by 10
Self-citationCapturing heterotachy through multi-gamma site modelsPreprint
Bouckaert, R., & Lockhart, P. J. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/018101 · Cited by 9
Self-citationSynthetic Language Generation and Model Validation in BEAST2Preprint
Bradley, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.07931 · Cited by 1
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Mendes, F. K., Bouckaert, R., & Drummond, A. J. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1470285 · Cited by 1
Self-citation
Flagged first, then by citations
5 of the 130 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Vaughan TG, Leventhal GE, Rasmussen DA, Drummond AJ, Welch D, Stadler T. Directly estimating epidemic curves from genomic data. bioRxiv. 2017; p. 142570.
- Matzke NJ, Wright A. Ground truthing tip-dating methods using fossil Canidae reveals major differences in performance. bioRxiv. 2016; p. 049643.
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Pond, S. L. K., Poon, A. F. Y., Velazquez, R., Weaver, S., Hepler, N. L., Murrell, B., Shank, S. D., Magalis, B. R., Bouvier, D., Nekrutenko, A., et al. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz197 · Cited by 767
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Metadata
- Authors
- Bouckaert, R., Vaughan, T. G., Barido-Sottani, J., Duchêne, S., Fourment, M., Gavryushkina, A., Heled, J., Jones, G., Kühnert, D., De Maio, N., Matschiner, M., Mendes, F. K., Müller, N. F., Ogilvie, H. A., du Plessis, L., Popinga, A., Rambaut, A., Rasmussen, D., Siveroni, I., Suchard, M. A., Wu, C.-H., Xie, D., Zhang, C., Stadler, T., & Drummond, A. J.
- Affiliations
- University of Auckland (NZ)
- SIB Swiss Institute of Bioinformatics (CH)
- The University of Melbourne (AU)
- University of Technology Sydney (AU)
- University of Otago (NZ)
- University of Gothenburg (SE)
- Max Planck Institute of Geoanthropology (DE)
- University of Basel (CH)
- Rice University (US)
- University of Oxford (GB)
- University of Edinburgh (GB)
- North Carolina State University (US)
- Imperial College London (GB)
- University of California, Los Angeles (US)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Bouckaert, Remco
- Vaughan, Timothy G.
- Duchêne, Sebastián
- Fourment, Mathieu
- Gavryushkina, Alexandra
- Heled, Joseph
- Jones, Graham
- Kühnert, Denise
- Matschiner, Michael
- Mendes, Fábio K.
- Müller, Nicola F.
- Ogilvie, Huw A.
- du Plessis, Louis
- Popinga, Alex
- Rambaut, Andrew
- Rasmussen, David
- Siveroni, Igor
- Suchard, Marc A.
- Wu, Chieh-Hsi
- Xie, Dong
- Zhang, Chi
- Stadler, Tanja
- Drummond, Alexei J.
- ORCID
- Bouckaert, Remco
- Vaughan, Timothy G.
- Barido-Sottani, Joëlle
- Duchêne, Sebastián
- Fourment, Mathieu
- Gavryushkina, Alexandra
- Heled, Joseph
- Jones, Graham
- Kühnert, Denise
- De Maio, Nicola
- Matschiner, Michael
- Mendes, Fábio K.
- Müller, Nicola F.
- Ogilvie, Huw A.
- du Plessis, Louis
- Popinga, Alex
- Rambaut, Andrew
- Rasmussen, David
- Siveroni, Igor
- Suchard, Marc A.
- Wu, Chieh-Hsi
- Xie, Dong
- Zhang, Chi
- Stadler, Tanja
- Drummond, Alexei J.
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2019-04-08
- Volume
- 15
- Issue
- 4
- Pages
- e1006650
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC6472827
- OpenAlex
- W2901954177
- Cited by
- 4,556
- References
- 130
- Citations by year
- 2026: 607 · 2025: 744 · 2024: 694 · 2023: 667 · 2022: 721 · 2021: 638 · 2020: 435 · 2019: 47
- FWCI
- 152.40
- Type
- journal article
- Language
- en
- Update notices
- new_version (2019-04-18)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (publishedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, 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)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion, cc-by)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Rice Research Repository (Rice University) (submittedVersion, cc-by)
- ResearchSpace (University of Auckland) (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Marsden Fund (16-UOA-277)
- 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, model hierarchy, software design, model compatibility, genome sequence, sampling date, radiocarbon dates, fossil occurrences, phenotypic data
- MeSH terms
- Animals, Bayes Theorem, Computer Simulation, Biological Evolution, Humans, Markov Chains, Models, Genetic, Monte Carlo Method, Phylogeny, Software, Evolution, Molecular, Computational Biology
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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