ArticleMolecular biology and evolution2023
Shrinkage-based Random Local Clocks with Scalable Inference.
Article in Molecular biology and evolution, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 15 citations in OpenAlex.
- Three thousand five hundred years of sheeppox virus evolution inferred from archaeological and codicological genomes.Science advances · 2026Article
- BEAST X for Bayesian phylogenetic, phylogeographic and phylodynamic inference.Nature methods · 2025Article
- Detecting Evolutionary Change-Points with Branch-Specific Substitution Models and Shrinkage Priors.ArXiv · 2025Article
- Detecting Evolutionary Change-Points with Branch-Specific Substitution Models and Shrinkage Priors.Research square · 2025Article
- Bayesian phylodynamic inference of population dynamics with dormancy.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Bayesian phylodynamic inference of population dynamics with dormancy.bioRxiv : the preprint server for biology · 2025Article
- Random-Effects Substitution Models for Phylogenetics via Scalable Gradient Approximations.Systematic biology · 2024Article
- Dating in the Dark: Elevated Substitution Rates in Cave Cockroaches (Blattodea: Nocticolidae) Have Negative Impacts on Molecular Date Estimates.Systematic biology · 2024Article
- Modeling Substitution Rate Evolution across Lineages and Relaxing the Molecular Clock.Genome biology and evolution · 2024Review
- A phylogenetic method linking nucleotide substitution rates to rates of continuous trait evolution.PLoS computational biology · 2024Article
- Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models.PLoS computational biology · 2024Article
- Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models.bioRxiv : the preprint server for biology · 2023Article
- Detecting Episodic Evolution through Bayesian Inference of Molecular Clock Models.Molecular biology and evolution · 2023Article
- The Emergence of SARS-CoV-2 Variants of Concern Is Driven by Acceleration of the Substitution Rate.Molecular biology and evolution · 2022Article
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Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
Abstract
Molecular clock models undergird modern methods of divergence-time estimation. Local clock models propose that the rate of molecular evolution is constant within phylogenetic subtrees. Current local clock inference procedures exhibit one or more weaknesses, namely they achieve limited scalability to trees with large numbers of taxa, impose model misspecification, or require a priori knowledge of the existence and location of clocks. To overcome these challenges, we present an autocorrelated, Bayesian model of heritable clock rate evolution that leverages heavy-tailed priors with mean zero to shrink increments of change between branch-specific clocks. We further develop an efficient Hamiltonian Monte Carlo sampler that exploits closed form gradient computations to scale our model to large trees. Inference under our shrinkage clock exhibits a speed-up compared to the popular random local clock when estimating branch-specific clock rates on a variety of simulated datasets. This speed-up increases with the size of the problem. We further show our shrinkage clock recovers known local clocks within a rodent and mammalian phylogeny. Finally, in a problem that once appeared computationally impractical, we investigate the heritable clock structure of various surface glycoproteins of influenza A virus in the absence of prior knowledge about clock placement. We implement our shrinkage clock and make it publicly available in the BEAST software package.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.