ArticleSystematic biology2018
Effective Online Bayesian Phylogenetics via Sequential Monte Carlo with Guided Proposals.
Article in Systematic biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Infinite Mixture Models for Improved Modeling of Across-Site Evolutionary Variation.Molecular biology and evolution · 2025Article
- Phylogenetic Tree Instability After Taxon Addition: Empirical Frequency, Predictability, and Consequences For Online Inference.Systematic biology · 2025Article
- An efficient coalescent model for heterochronously sampled molecular data.Journal of the American Statistical Association · 2024Article
- Sparse Neighbor Joining: rapid phylogenetic inference using a sparse distance matrix.Bioinformatics (Oxford, England) · 2024Article
- Ranked Subtree Prune and Regraft.Bulletin of mathematical biology · 2024Article
- Online tree expansion could help solve the problem of scalability in Bayesian phylogenetics.Systematic biology · 2023Article
- Online Phylogenetics with matOptimize Produces Equivalent Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies than de novo and Maximum-Likelihood Implementations.Systematic biology · 2023Article
- Fidelity of hyperbolic space for Bayesian phylogenetic inference.PLoS computational biology · 2023Article
- Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves.Infectious Disease Modelling · 2023Article
- Data integration in Bayesian phylogenetics.Annual review of statistics and its application · 2023Article
- Scalable Bayesian phylogenetics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Review
- Online Phylogenetics using Parsimony Produces Slightly Better Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies thanbioRxiv : the preprint server for biology · 2022Article
- Real-Time and Remote MCMC Trace Inspection with Beastiary.Molecular biology and evolution · 2022Article
- Statistical Challenges in Tracking the Evolution of SARS-CoV-2.Statistical science : a review journal of the Institute of Mathematical Statistics · 2022Article
- CATHAI: cluster analysis tool for healthcare-associated infections.Bioinformatics advances · 2022Article
- Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications.Virus evolution · 2022Review
- Additive Uncorrelated Relaxed Clock Models for the Dating of Genomic Epidemiology Phylogenies.Molecular biology and evolution · 2021Article
- Demographic inference from multiple whole genomes using a particle filter for continuous Markov jump processes.PloS one · 2021Article
- Online Bayesian Phylodynamic Inference in BEAST with Application to Epidemic Reconstruction.Molecular biology and evolution · 2020Article
- Reproducible parallel inference and simulation of stochastic state space models using odin, dust, and mcstate.Wellcome open research · 2020Article
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6 authors.
Funding
Abstract
Modern infectious disease outbreak surveillance produces continuous streams of sequence data which require phylogenetic analysis as data arrives. Current software packages for Bayesian phylogenetic inference are unable to quickly incorporate new sequences as they become available, making them less useful for dynamically unfolding evolutionary stories. This limitation can be addressed by applying a class of Bayesian statistical inference algorithms called sequential Monte Carlo (SMC) to conduct online inference, wherein new data can be continuously incorporated to update the estimate of the posterior probability distribution. In this article, we describe and evaluate several different online phylogenetic sequential Monte Carlo (OPSMC) algorithms. We show that proposing new phylogenies with a density similar to the Bayesian prior suffers from poor performance, and we develop "guided" proposals that better match the proposal density to the posterior. Furthermore, we show that the simplest guided proposals can exhibit pathological behavior in some situations, leading to poor results, and that the situation can be resolved by heating the proposal density. The results demonstrate that relative to the widely used MCMC-based algorithm implemented in MrBayes, the total time required to compute a series of phylogenetic posteriors as sequences arrive can be significantly reduced by the use of OPSMC, without incurring a significant loss in accuracy.
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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.