Cited by 21,937Genomics and Phylogenetic StudiesGenetic diversity and population structureMarkov Chains and Monte Carlo Methods

MRBAYES: Bayesian inference of phylogenetic trees.

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Huelsenbeck, John P., Ronquist, Fredrik·2001·Bioinformatics

Abstract

Summary: The program MRBAYES performs Bayesian inference of phylogeny using a variant of Markov chain Monte Carlo. Availability: MRBAYES, including the source code, documentation, sample data files, and an executable, is available at http://brahms.biology.rochester.edu/software.html. Contact: johnh@brahms.biology.rochester.edu

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  • Under the independent-rate assumption, it is possible to infer phylogenies but not to estimate molecular rates or divergence times, because the individual contributions of rate and time cannot be separated.

    “Under such an assumption, it is possible to infer phylogenies (e.g., [ 9, 10]), but not to estimate molecular rates or divergence times, because the individual contributions of rate and time to molecular evolution cannot be separated.”

    From Relaxed Phylogenetics and Dating with Confidence (2006) · cites this paper as [9]

  • Phylogenetic gene trees were built using MrBayes with the LG substitution model, model parameters estimated with ProtTest3, and simulations stopped after at least 1M iterations passed after split frequency between runs reached below 0.01.

    “Phylogenetic gene trees were built using the MrBayes package (Bayesian inference) 61 modified to incorporate the LG substitution model.Model parameters were estimated using the ProtTest3 package 62 .MrBayes was set to run two sets of four Markov chains.The simulations were stopped once, at least 1 M iterations passed after split frequency between the two runs reached level lower than 0.01.”
  • Methods for inferring latent phylogenetic trees are frustrated by long run times and poor scalability to large datasets.

    “Methods for inferring these latent phylogenetic trees continue to be of substantial interest 92-94, but are frustrated by long run times and poor scalability to large datasets.”

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Research works printed but not linked: 2

  1. Mau,B. (1996) Bayesian phylogenetic inference via Markov chain Monte carlo methods, PhD Dissertation, University of Wiscon- sin, Madison.
  2. Swofford,D., Olsen,G., Waddell,P. and Hillis,D.M. (1996) Phyloge- netic inference. In Hillis, Moritz and Mable (eds), Molecular Systematics, 2nd edition.. Sinauer, Sunderland, MA, pp. 407- 511.

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Authors
Huelsenbeck, J. P., & Ronquist, F.
Affiliations
  • University of Rochester (US)
  • Uppsala University (SE)
Venue
Bioinformatics
Published
2001-08-01
Volume
17
Issue
8
Pages
754-755
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
OpenAlex
W2161444534
Cited by
21,937
References
14
Citations by year
2026: 365 · 2025: 531 · 2024: 640 · 2023: 709 · 2022: 767 · 2021: 1031 · 2020: 919 · 2019: 858 · 2018: 922 · 2017: 962 · 2016: 988 · 2015: 1000 · 2014: 1120 · 2013: 1222 · 2012: 1320
FWCI
123.27
Type
journal article
Language
en
Open access
Locations
  • Bioinformatics (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Science Foundation (0075406)
  • Division of Environmental Biology
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
  • Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
Keywords
MrBayes, Bayesian phylogenetics, phylogenetic tree estimation, Markov chain Monte Carlo, molecular phylogeny
MeSH terms
Algorithms, Bayes Theorem, Markov Chains, Phylogeny, Software, Computational Biology

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