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Cited by 4,556Genomics and Phylogenetic StudiesGenetic diversity and population structureEvolution and Genetic Dynamics

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BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis

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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.·2019·PLOS Computational Biology
DOI 10.1371/journal.pcbi.1006650PMC PMC6472827

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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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Figures

Fig 1 Phylogenetic structures available in BEAST 2. (a) A tip-dated time tree, with leaf times as boundary conditions but not data (generally a coalescent prior is applied in this setting). (b) A species tree with one or more embedded gene trees (c) A multi-type time tree has measured types at the leaves and the type changes that paint the ancestral lineages in the tree are sampled as latent variables by MCMC. (d) A sampled ancestor tree, with two types of sampling events: extinct species (red) and extant species (blue). Extinct species can be leaves or, if they are the direct ancestor of another sample, degree-2 sampled ancestor nodes. (e) An ancestral gene conversion graph is composed of a clonal frame (solid time tree) and an extra edge and gene boundaries for each gene conversion event. (f) A species network with one or more embedded gene trees.
Fig 1 Phylogenetic structures available in BEAST 2. (a) A tip-dated time tree, with leaf times as boundary conditions but not data (generally a coalescent prior is applied in this setting). (b) A species tree with one or more embedded gene trees (c) A multi-type time tree has measured types at the leaves and the type changes that paint the ancestral lineages in the tree are sampled as latent variables by MCMC. (d) A sampled ancestor tree, with two types of sampling events: extinct species (red) and extant species (blue). Extinct species can be leaves or, if they are the direct ancestor of another sample, degree-2 sampled ancestor nodes. (e) An ancestral gene conversion graph is composed of a clonal frame (solid time tree) and an extra edge and gene boundaries for each gene conversion event. (f) A species network with one or more embedded gene trees.CC BY · PubMed Central
Fig 2 bModelTest analysis for 36 mammalian species [50]. a) Posterior distribution of substitution models. Each circle represents a substitution model indicated by a six digit number corresponding to the six rates of reversible substitution models. In alphabetical order, these are A→C, A→G, A→T, C→G, C→T, and G→T, which can be shared in groups. The six digit numbers indicate these groupings, for example 121121 indicates the HKY model, which has shared rates for transitions and shared rates for transversions. Here, only models are considered that are reversible and do not share transition and transversion rates (with the exception of the JC69 and F81 models). Other substitution model sets are available. Links between substitution models indicate possible jumps during the MCMC chain from simpler (tail of arrow) to more complex (head of arrow) models and back. There is no single preferred substitution model for this data, as the posterior probability is spread over a number of alternative substitution models. Blue circles indicate the eight models contained in the 95% credible set, models with red circles are outside of this set, and models without circles have negligible support. b) Posterior tree distribution resulting from the bModelTest analysis.
Fig 2 bModelTest analysis for 36 mammalian species [50]. a) Posterior distribution of substitution models. Each circle represents a substitution model indicated by a six digit number corresponding to the six rates of reversible substitution models. In alphabetical order, these are A→C, A→G, A→T, C→G, C→T, and G→T, which can be shared in groups. The six digit numbers indicate these groupings, for example 121121 indicates the HKY model, which has shared rates for transitions and shared rates for transversions. Here, only models are considered that are reversible and do not share transition and transversion rates (with the exception of the JC69 and F81 models). Other substitution model sets are available. Links between substitution models indicate possible jumps during the MCMC chain from simpler (tail of arrow) to more complex (head of arrow) models and back. There is no single preferred substitution model for this data, as the posterior probability is spread over a number of alternative substitution models. Blue circles indicate the eight models contained in the 95% credible set, models with red circles are outside of this set, and models without circles have negligible support. b) Posterior tree distribution resulting from the bModelTest analysis.CC BY · PubMed Central
Fig 3 Birth-death skyline (bdsky) analysis of the 2013–2016 West African Ebola virus disease epidemic. (a) The maximum clade credibility tree of the 811 sequences used in the analysis. (b) The median posterior estimate of the estimated effective reproductive number (Re) over time is shown in orange, with the 95% highest posterior density (HPD) interval in orange shading. The red dotted line indicates the epidemic threshold (Re = 1). If Re is below this threshold the epidemic has reached a turning point and is no longer spreading. The posterior distribution of the origin time of the epidemic (t0) is shown in green. The number of laboratory-confirmed cases per week is shown in blue. Red arrows indicate weeks with fewer than 10 confirmed cases. The dotted line at A indicates the onset of symptoms in the suspected index case (see text for details). The dotted lines at B and C indicate the dates at which the WHO declared an Ebola virus disease outbreak in Guinea and a Public Health Emergency of International Concern (PHEIC), respectively. The dotted line at D indicates the first time any of the three countries with intense transmission (Liberia) was declared Ebola free following 42 days without any new infections being reported (new cases were subsequently detected in Liberia in June 2015). (c) The median posterior estimate of the monthly sampling proportion is shown in purple, with the 95% HPD interval in purple shading. The red dashed line indicates the number of sampled sequences in the dataset, divided by the number of laboratory-confirmed cases, for each month in the analysis. This serves as an empirical estimate of the true sampling proportion. The posterior distributions and medians (dashed lines) of the infected period and the mean clock rate (truncated at the 95% HPD limits) are shown in panels (d) and (e).
Fig 3 Birth-death skyline (bdsky) analysis of the 2013–2016 West African Ebola virus disease epidemic. (a) The maximum clade credibility tree of the 811 sequences used in the analysis. (b) The median posterior estimate of the estimated effective reproductive number (Re) over time is shown in orange, with the 95% highest posterior density (HPD) interval in orange shading. The red dotted line indicates the epidemic threshold (Re = 1). If Re is below this threshold the epidemic has reached a turning point and is no longer spreading. The posterior distribution of the origin time of the epidemic (t0) is shown in green. The number of laboratory-confirmed cases per week is shown in blue. Red arrows indicate weeks with fewer than 10 confirmed cases. The dotted line at A indicates the onset of symptoms in the suspected index case (see text for details). The dotted lines at B and C indicate the dates at which the WHO declared an Ebola virus disease outbreak in Guinea and a Public Health Emergency of International Concern (PHEIC), respectively. The dotted line at D indicates the first time any of the three countries with intense transmission (Liberia) was declared Ebola free following 42 days without any new infections being reported (new cases were subsequently detected in Liberia in June 2015). (c) The median posterior estimate of the monthly sampling proportion is shown in purple, with the 95% HPD interval in purple shading. The red dashed line indicates the number of sampled sequences in the dataset, divided by the number of laboratory-confirmed cases, for each month in the analysis. This serves as an empirical estimate of the true sampling proportion. The posterior distributions and medians (dashed lines) of the infected period and the mean clock rate (truncated at the 95% HPD limits) are shown in panels (d) and (e).CC BY · PubMed Central
Fig 4 The multispecies coalescent (MSC) model with three species and a single gene tree. A separate coalescent process applies to each of the five branches in the tree; the branches for the extant species A (red), B (green) and C (blue), the ancestral branch of A and B (yellow), and the root branch (grey). Several individuals have been sampled per species. In this example the ancestral lineage of individual b4 does not coalesce in species B or ancestral species 4. In ancestral species 5, it coalesces with the ancestral lineage of species C. This leads to incomplete lineage sorting and enables gene tree discordance—in this example b4 is a sister taxon to individuals from species C, rather than to individuals from its own species, or sister species A. If b4 was the representative individual for its species, then this gene would exhibit gene tree discordance. Other individuals which show concordance at this locus are expected to show discordance at other unlinked loci when populations are large or speciation times are recent.
Fig 4 The multispecies coalescent (MSC) model with three species and a single gene tree. A separate coalescent process applies to each of the five branches in the tree; the branches for the extant species A (red), B (green) and C (blue), the ancestral branch of A and B (yellow), and the root branch (grey). Several individuals have been sampled per species. In this example the ancestral lineage of individual b4 does not coalesce in species B or ancestral species 4. In ancestral species 5, it coalesces with the ancestral lineage of species C. This leads to incomplete lineage sorting and enables gene tree discordance—in this example b4 is a sister taxon to individuals from species C, rather than to individuals from its own species, or sister species A. If b4 was the representative individual for its species, then this gene would exhibit gene tree discordance. Other individuals which show concordance at this locus are expected to show discordance at other unlinked loci when populations are large or speciation times are recent.CC BY · PubMed Central
Fig 5 AIM analysis of 100 nuclear gene alignments for the five Princess cichlid species. Species are Neolamprologus marunguensis, N. gracilis, N. brichardi, N. olivaceous, N. pulcher, as well as the outgroup Metriaclima zebra. a) to d) show the best-supported tree topologies. Arrows show directions of gene flow that are supported with a Bayes Factor of more than 10. Trees a) and c) only differ in the timing of the speciation events; however, AIM differentiates between differently ranked topologies, since these have to be characterized by using different parameters.
Fig 5 AIM analysis of 100 nuclear gene alignments for the five Princess cichlid species. Species are Neolamprologus marunguensis, N. gracilis, N. brichardi, N. olivaceous, N. pulcher, as well as the outgroup Metriaclima zebra. a) to d) show the best-supported tree topologies. Arrows show directions of gene flow that are supported with a Bayes Factor of more than 10. Trees a) and c) only differ in the timing of the speciation events; however, AIM differentiates between differently ranked topologies, since these have to be characterized by using different parameters.CC BY · PubMed Central
Fig 6 Posterior predictive distributions for two phylodynamic models. The right column shows the trajectories of the reproductive number over time for a set of 100 publicly available genomes from the 2009 H1N1 influenza pandemic in North America using stochastic (birth-death SIR; [28]) and deterministic (deterministic coalescent SIR [27]) models. Each blue line is a trajectory sampled from the posterior distribution. The models make different inferences of when the reproductive number falls below 1 (vertical dotted line; the horizontal dashed line is for R = 1), indicating that the pandemic is past its infectious peak. The right column shows the posterior predictive distributions of the root height for both models (grey histograms) and the value for the empirical data (orange vertical lines). Trees simulated from the stochastic model produce trees that are more consistent with the empirical tree than those from the deterministic model, suggesting that stochasticity may play an important role in the early stages of the pandemic (samples were collected up to June 2009).
Fig 6 Posterior predictive distributions for two phylodynamic models. The right column shows the trajectories of the reproductive number over time for a set of 100 publicly available genomes from the 2009 H1N1 influenza pandemic in North America using stochastic (birth-death SIR; [28]) and deterministic (deterministic coalescent SIR [27]) models. Each blue line is a trajectory sampled from the posterior distribution. The models make different inferences of when the reproductive number falls below 1 (vertical dotted line; the horizontal dashed line is for R = 1), indicating that the pandemic is past its infectious peak. The right column shows the posterior predictive distributions of the root height for both models (grey histograms) and the value for the empirical data (orange vertical lines). Trees simulated from the stochastic model produce trees that are more consistent with the empirical tree than those from the deterministic model, suggesting that stochasticity may play an important role in the early stages of the pandemic (samples were collected up to June 2009).CC BY · PubMed Central

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Research works printed but not linked: 2

  1. Vaughan TG, Leventhal GE, Rasmussen DA, Drummond AJ, Welch D, Stadler T. Directly estimating epidemic curves from genomic data. bioRxiv. 2017; p. 142570.
  2. 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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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
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
DOI
10.1371/journal.pcbi.1006650
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
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

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