Biology subjects

Huelsenbeck, J. P.

Publications and source records attributed to Huelsenbeck, J. P..

3 recordsLinked to original sources

bioRxiv2026

Estimating Divergence Times and Diversification Rates with the Unresolved Fossilized Birth-Death Process

Tip-dating using Fossilized Birth-Death (FBD) models offers a favorable alternative to conventional node-dating analyses of the evolutionary timescale, bypassing various conceptual and technical difficulties. However, despite their increasing popularity in phylogenetic studies, their use has remained rather limited in taxonomic scope because of the difficulty in collecting morphological data, which is often required for tip-dating analyses, and computational intractability when a large number of fossils are present. Recent studies, however, suggest that FBD models can also be used in a principled way as priors for molecular clock analyses, even in the absence of morphological data, relying only on temporal information and given clade assignments. Heath et al. (2014) introduced an "unresolved" FBD model in which uncertainty in fossil placements is analytically integrated out when morphological data are unavailable, providing a potential solution to this problem. Here, we show that the topological enumeration scheme used by Heath et al. (2014) to marginalize over unresolved fossil attachments overcounts configurations in which multiple fossil taxa can be resolved as a clade, and we provide a correction to this factor, yielding a valid marginalized unresolved likelihood. We apply our implementation to empirical datasets of Cetacea, Emydidae, and Palaeodictyopterida, and show that our method converges and yields reasonable estimates within a reasonable amount of time, even when hundreds of fossils are present.

evolutionary biology↗

bioRxiv2019

Using Parsimony-Guided Tree Proposals to Accelerate Convergence in Bayesian Phylogenetic Inference

Sampling across tree space is one of the major challenges in Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) algorithms. Standard MCMC tree moves consider small random perturbations of the topology, and select from candidate trees at random or based on the distance between the old and new topologies. MCMC algorithms using such moves tend to get trapped in tree space, making them slow in finding the globally most probable trees (known as convergence) and in estimating the correct proportions of the different types of them (known as mixing). Here, we introduce a new class of moves, which propose trees based on their parsimony scores. The proposal distribution derived from the parsimony scores is a quickly computable albeit rough approximation of the conditional posterior distribution over candidate trees. We demonstrate with simulations that parsimony-guided moves correctly sample the uniform distribution of topologies from the prior. We then evaluate their performance against standard moves using six challenging empirical datasets, for which we were able to obtain accurate reference estimates of the posterior using long MCMC runs, a mix of topology proposals, and Metropolis coupling. On these datasets, ranging in size from 357 to 934 taxa and from 1,740 to 5,681 sites, we find that single chains using parsimony-guided moves usually converge an order of magnitude faster than chains using standard moves. They also exhibit better mixing, that is, they cover the most probable trees more quickly. Our results show that tree moves based on quick and dirty estimates of the posterior probability can significantly outperform standard moves. Future research will have to show to what extent the performance of such moves can be improved further by finding better ways of approximating the posterior probability, taking the trade-off between accuracy and speed into account.

evolutionary biology↗

bioRxiv2017

Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics

MotivationIn Bayesian phylogenetic inference, marginal likelihoods are estimated using either the path-sampling or stepping-stone-sampling algorithms. Both algorithms are computationally demanding because they require a series of power posterior Markov chain Monte Carlo (MCMC) simulations. Here we introduce a general parallelization strategy that distributes the power posterior MCMC simulations and the likelihood computations over available CPUs. Our parallelization strategy can easily be applied to any statistical model despite our primary focus on molecular substitution models in this study.\n\nResultsUsing two phylogenetic example datasets, we demonstrate that the runtime of the marginal likelihood estimation can be reduced significantly even if only two CPUs are available (an average performance increase of 1.96x). The performance increase is nearly linear with the number of available CPUs. We record a performance increase of 11.4x for cluster nodes with 16 CPUs, representing a substantial reduction to the runtime of marginal likelihood estimations. Hence, our parallelization strategy enables the estimation of marginal likelihoods to complete in a feasible amount of time which previously needed days, weeks or even months.\n\nAvailabilityThe methods described here are implemented in our open-source software RevBayes which is available from http://www.RevBayes.com.\n\nContactSebastian.Hoehna@gmail.com\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗