ReviewPhilosophical transactions of the Royal Society of London. Series B, Biological sciences2022
Scalable Bayesian phylogenetics.
Review in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Characteristics of Mitochondrial Genomes and Phylogenetic Analysis of Three Species of Littorinimorpha Snails.Animals : an open access journal from MDPI · 2026Article
- Article
- Global approaches to infectious disease surveillance and modeling.Nature medicine · 2026Review
- Global, regional, and national burden of Early-onset Parkinson's disease: A comprehensive analysis and forecast, 1990-2021.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Article
- VINE: Variational inference for scalable Bayesian reconstruction of species and cell-lineage phylogenies.bioRxiv : the preprint server for biology · 2026Article
- Article
- Online tree expansion could help solve the problem of scalability in Bayesian phylogenetics.Systematic biology · 2023Article
- SARS-CoV-2 in low-income countries: the need for sustained genomic surveillance.The Lancet. Global health · 2023Article
- Model design for nonparametric phylodynamic inference and applications to pathogen surveillance.Virus evolution · 2023Article
- Genomic population structures of microbial pathogens.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Recent advances in Bayesian phylogenetics offer substantial computational savings to accommodate increased genomic sampling that challenges traditional inference methods. In this review, we begin with a brief summary of the Bayesian phylogenetic framework, and then conceptualize a variety of methods to improve posterior approximations via Markov chain Monte Carlo (MCMC) sampling. Specifically, we discuss methods to improve the speed of likelihood calculations, reduce MCMC burn-in, and generate better MCMC proposals. We apply several of these techniques to study the evolution of HIV virulence along a 1536-tip phylogeny and estimate the internal node heights of a 1000-tip SARS-CoV-2 phylogenetic tree in order to illustrate the speed-up of such analyses using current state-of-the-art approaches. We conclude our review with a discussion of promising alternatives to MCMC that approximate the phylogenetic posterior. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.
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Registered trials
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.