Over het archief
Het OWA, het open archief van het Waterbouwkundig Laboratorium heeft tot doel alle vrij toegankelijke onderzoeksresultaten van dit instituut in digitale vorm aan te bieden. Op die manier wil het de zichtbaarheid, verspreiding en gebruik van deze onderzoeksresultaten, alsook de wetenschappelijke communicatie maximaal bevorderen.
Dit archief wordt uitgebouwd en beheerd volgens de principes van de Open Access Movement, en het daaruit ontstane Open Archives Initiative.
Basisinformatie over ‘Open Access to scholarly information'.
| one publication added to basket [382911] |
Scalable Bayesian divergence time estimation with ratio transformations
Ji, X.; Fisher, A.A.; Su, S.; Thorne, J.L.; Potter, B.; Lemey, P.; Baele, G.; Suchard, M.A. (2023). Scalable Bayesian divergence time estimation with ratio transformations. Syst. Biol. 72(5): 1136-1153. https://dx.doi.org/10.1093/sysbio/syad039
In: Systematic Biology. Oxford University Press: Washington, D.C.. ISSN 1063-5157; e-ISSN 1076-836X, meer
|  |
| Author keywords |
Bayesian inference; divergence time estimation; effective sample size; Hamiltonian Monte Carlo; pathogens; phylogenetics; ratio transformation |
| Auteurs | | Top |
- Ji, X.
- Fisher, A.A.
- Su, S.
- Thorne, J.L.
|
- Potter, B., meer
- Lemey, P., meer
- Baele, G., meer
- Suchard, M.A.
|
|
| Abstract |
Divergence time estimation is crucial to provide temporal signals for dating biologically important events from species divergence to viral transmissions in space and time. With the advent of high-throughput sequencing, recent Bayesian phylogenetic studies have analyzed hundreds to thousands of sequences. Such large-scale analyses challenge divergence time reconstruction by requiring inference on highly correlated internal node heights that often become computationally infeasible. To overcome this limitation, we explore a ratio transformation that maps the original N -1 internal node heights into a space of one height parameter and N-2 ratio parameters. To make the analyses scalable, we develop a collection of linear-time algorithms to compute the gradient and Jacobian-associated terms of the log-likelihood with respect to these ratios. We then apply Hamiltonian Monte Carlo sampling with the ratio transform in a Bayesian framework to learn the divergence times in 4 pathogenic viruses (West Nile virus, rabies virus, Lassa virus, and Ebola virus) and the coralline red algae. Our method both resolves a mixing issue in the West Nile virus example and improves inference efficiency by at least 5-fold for the Lassa and rabies virus examples as well as for the algae example. Our method now also makes it computationally feasible to incorporate mixed-effects molecular clock models for the Ebola virus example, confirms the findings from the original study, and reveals clearer multimodal distributions of the divergence times of some clades of interest. [Bayesian inference; divergence time estimation; effective sample size; Hamiltonian Monte Carlo; pathogens; phylogenetics; ratio transformation.] |
IMIS is ontwikkeld en wordt gehost door het VLIZ.
