2021/10/25 by Xiang Ji, Alexander A. Fisher, Ji, Xiang +13 · 1 citation
Biochemistry, Genetics and Molecular Biology · Medicine · #Computation (stat.CO) #Data-Driven Disease Surveillance #FOS: Biological sciences #FOS: Computer and information sciences #Genomics and Phylogenetic Studies #Populations and Evolution (q-bio.PE) #Yersinia bacterium, plague, ectoparasites research
paper · pdf · doi:10.48550/arxiv.2110.13298
openalex publication_date 2021/10/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 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 four pathogenic virus phylogenies: West Nile virus, rabies virus, Lassa virus and Ebola virus. 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. Our method also makes it now 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.