P3: Phylogenetic Posterior Prediction in RevBayes

Höhna, Sebastian; Coghill, Lyndon M.; Mount, Genevieve G.; Thomson, Robert C.; Brown, Jeremy M.

doi:10.1093/molbev/msx286

Cited by 37 publications

(53 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2018). Therefore, methods of assessing model adequacy are likely to be useful complementary diagnostics for improving the accuracy of topological inferences (Brown and ElDabaje 2009; Doyle et al 2015; Höhna et al 2017; Duchêne et al 2018b, 2018c).…”

Section: Discussionmentioning

confidence: 99%

Phylogenetic signal is associated with the degree of variation in root-to-tip distances

Vankan¹,

Pardo‐Díaz

et al. 2020

Preprint

View full text Add to dashboard Cite

The phylogenetic information contained in sequence data is partly determined by the overall rate of nucleotide substitution in the genomic region in question. However, phylogenetic signal is affected by various other factors, such as heterogeneity in substitution rates across lineages. These factors might be able to predict the phylogenetic accuracy of any given gene in a data set. We examined the association between the accuracy of phylogenetic inference across genes and several characteristics of branch lengths in phylogenomic data. In a large number of published data sets, we found that the accuracy of phylogenetic inference from genes was consistently associated with their mean statistical branch support and variation in their gene tree root-to-tip distances, but not with tree length and stemminess. Therefore, a signal of constant evolutionary rates across lineages appears to be beneficial for phylogenetic inference. Identifying the causes of variation in root-to-tip lengths in gene trees also offers a potential way forward to increase congruence in the signal across genes and improve estimates of species trees from phylogenomic data sets.

show abstract

Section: Discussionmentioning

confidence: 99%

Phylogenetic signal is associated with the degree of variation in root-to-tip distances

Vankan¹,

Pardo‐Díaz

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In the next section, 110 we will highlight the generative priors for the first four classes of extended phylogenetic 111 structures as well as recent advances on new models for classic binary rooted time trees. 112 In addition, some of us (TGV, TS) are currently working on including time tree 113 polytomies in BEAST 2, as may be relevant to, for example, super-spreading events in 114 infectious disease. 115 New models 116 A Bayesian phylodynamic analysis requires the specification of a model for 117 substitutions, a clock model, and a population dynamic model generating the 118 phylogenetic structure, whether that be a tree, a phylogenetic network or a hierarchical 119 combination of the two.…”

Section: Introduction 15mentioning

confidence: 99%

“…First, data is simulated using parameter values 605 sampled from the posterior distribution. Such simulations are known as posterior 606 predictive simulations [111][112][113]. A test statistic is calculated for the empirical data and 607 for the simulated data.…”

mentioning

confidence: 99%

BEAST 2.5: An Advanced Software Platform for Bayesian Evolutionary Analysis

Bouckaert

Vaughan

Barido‐Sottani

et al. 2018

Preprint

View full text Add to dashboard Cite

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 r 15, 2018 1/29 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 summaryBayesian phylogenetic inference methods have undergone considerable development in 1 recent years, and joint modelling of rich evolutionary data, including genomes, 2 phenotypes and fossil occurrences is increasingly common. Advanced computational 3 software packages that allow robust development of compatible (sub-)models which can 4 be composed into a full model hierarchy have played a key role in these developments. 5Developing scientific software is increasingly crucial to advancement in many fields of 6 biology. The challenges range from practical software development and engineering, 7 distributed team coordination, conceptual development and statistical modelling, to 8 validation and testing. BEAST 2 is one such computational software platform for 9 phylogenetics, population genetics and phylodynamics, and was first announced over 4 10 years ago. Here we describe the full range of new tools and models available on the 11 BEAST 2.5 platform, which expand joint evolutionary inference in many new directions, 12 especially for joint inference over multiple data types, non-tree models and complex 13 phylodynamics. 14 24 LogAnalyser, LogCombiner, TreeAnnotator, DensiTree [3], as well as a package 25 manager. 26 Shortly after its release, a number of packages were added, such as MASTER for 27 simulating stochastic population dynamics models [4], MultiTypeTree for inferring 28 structured coalescent models [5], RBS for reversible jump across substitution models [6], 29 SNAPP for multi species coalescent over SNP data [7], subst-bma for Bayesian model 30 averaging over site models [8], and BDSKY for the birth-death skyline tree model [9]. 31 All these package...

show abstract

“…Bayesian model adequacy consists of a posterior predictive framework (Rubin 1981, 1984; Bollback 2002; Brown 2014a, 2014b; Lewis et al 2014; Höhna et al 2017). The posterior distribution of the model in question is approximated given the empirical data, for example using Markov chain Monte Carlo (MCMC).…”

Section: Bayesian Model Adequacymentioning

confidence: 99%

Phylodynamic model adequacy using posterior predictive simulations

Duchêne

Bouckaert

Duchêne

et al. 2018

Preprint

View full text Add to dashboard Cite

Rapidly evolving pathogens, such as viruses and bacteria, accumulate genetic change at a similar timescale over which their epidemiological processes occur, such that it is possible to make inferences about their infectious spread using phylogenetic time-trees. For this purpose it is necessary to choose a phylodynamic model. However, the resulting inferences are contingent on whether the model adequately describes key features of the data. Model adequacy methods allow formal rejection of a model if it cannot generate the main features of the data. We present TreeModelAdequacy (TMA), a package for the popular BEAST2 software, that allows assessing the adequacy of phylodynamic models. We illustrate its utility by analysing phylogenetic trees from two viral outbreaks of Ebola and H1N1 influenza. The main features of the Ebola data were adequately described by the coalescent exponential-growth model, whereas the H1N1 influenza data was best described by the birth-death SIR model.

show abstract

P3: Phylogenetic Posterior Prediction in RevBayes

Cited by 37 publications

References 22 publications

Phylogenetic signal is associated with the degree of variation in root-to-tip distances

Phylogenetic signal is associated with the degree of variation in root-to-tip distances

BEAST 2.5: An Advanced Software Platform for Bayesian Evolutionary Analysis

Phylodynamic model adequacy using posterior predictive simulations

Contact Info

Product

Resources

About