{"refrec":{"BRefID":382911,"RR":"<b>Ji, X.; Fisher, A.A.; Su, S.; Thorne, J.L.; Potter, B.; Lemey, P.; Baele, G.; Suchard, M.A.</b> (2023). Scalable Bayesian divergence time estimation with ratio transformations. <i>Syst. Biol. 72(5)</i>: 1136-1153. <a href=\"https://dx.doi.org/10.1093/sysbio/syad039\" target=\"_blank\">https://dx.doi.org/10.1093/sysbio/syad039</a>","BEntID":380653,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":0,"RefStringPartII":". <i>Syst. Biol. 72(5)</i>: 1136-1153. <a href=\"https://dx.doi.org/10.1093/sysbio/syad039\" target=\"_blank\">https://dx.doi.org/10.1093/sysbio/syad039</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Ji, X.; Fisher, A.A.; Su, S.; Thorne, J.L.; Potter, B.; Lemey, P.; Baele, G.; Suchard, M.A.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Ji, X. <i>et al.</i>","Englishabstract":"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.]","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Scalable Bayesian divergence time estimation with ratio transformations","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-04-25 01:33:45.226862","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":"Bayesian inference; divergence time estimation; effective sample size; Hamiltonian Monte Carlo; pathogens; phylogenetics; ratio transformation","OtherDescriptors":null,"Notes":null,"AnaPub":2023,"MonPub":null,"DateUpdate":"2024-02-19","DateCreate":"2024-02-16","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:001051314800001","VABBcode":null,"OpenAcc":0,"DOI":"10.1093/sysbio/syad039"},"refs":null,"anarec":{"AnaID":382911,"PubliDate":2023,"Pagination":"1136-1153","XtraPublOfAnaID":null,"ISBN":null,"Volume":"72","Issue":"5","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":43945,"SerRR":"Systematic Biology. 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