Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Effective Online Bayesian Phylogenetics Via Sequential Monte Carlo With Guided Proposals

View ORCID ProfileMathieu Fourment, Brian C. Claywell, Vu Dinh, Connor McCoy, Frederick A. Matsen IV, Aaron E. Darling
doi: https://doi.org/10.1101/145219
Mathieu Fourment
University of Technology Sydney;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Mathieu Fourment
  • For correspondence: m.fourment@gmail.com
Brian C. Claywell
Fred Hutchinson Cancer Research Center
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Vu Dinh
Fred Hutchinson Cancer Research Center
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Connor McCoy
Fred Hutchinson Cancer Research Center
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Frederick A. Matsen
Fred Hutchinson Cancer Research Center
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Aaron E. Darling
University of Technology Sydney;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Info/History
  • Metrics
  • Preview PDF
Loading

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 paper 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.

Copyright 
The copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
  • Posted June 2, 2017.

Download PDF

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Effective Online Bayesian Phylogenetics Via Sequential Monte Carlo With Guided Proposals
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
Share
Effective Online Bayesian Phylogenetics Via Sequential Monte Carlo With Guided Proposals
Mathieu Fourment, Brian C. Claywell, Vu Dinh, Connor McCoy, Frederick A. Matsen IV, Aaron E. Darling
bioRxiv 145219; doi: https://doi.org/10.1101/145219
del.icio.us logo Digg logo Reddit logo Technorati logo Twitter logo CiteULike logo Connotea logo Facebook logo Google logo Mendeley logo
Citation Tools
Effective Online Bayesian Phylogenetics Via Sequential Monte Carlo With Guided Proposals
Mathieu Fourment, Brian C. Claywell, Vu Dinh, Connor McCoy, Frederick A. Matsen IV, Aaron E. Darling
bioRxiv 145219; doi: https://doi.org/10.1101/145219

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (542)
  • Biochemistry (742)
  • Bioengineering (447)
  • Bioinformatics (4329)
  • Biophysics (1316)
  • Cancer Biology (890)
  • Cell Biology (1257)
  • Clinical Trials (43)
  • Developmental Biology (845)
  • Ecology (1456)
  • Epidemiology (702)
  • Evolutionary Biology (3437)
  • Genetics (2327)
  • Genomics (3012)
  • Immunology (479)
  • Microbiology (1935)
  • Molecular Biology (757)
  • Neuroscience (5755)
  • Paleontology (36)
  • Pathology (106)
  • Pharmacology and Toxicology (184)
  • Physiology (238)
  • Plant Biology (806)
  • Scientific Communication and Education (222)
  • Synthetic Biology (352)
  • Systems Biology (1192)
  • Zoology (148)