Bayesian phylogeny analysis via stochastic approximation Monte Carlo

Handle URI:
http://hdl.handle.net/10754/597656
Title:
Bayesian phylogeny analysis via stochastic approximation Monte Carlo
Authors:
Cheon, Sooyoung; Liang, Faming
Abstract:
Monte Carlo methods have received much attention in the recent literature of phylogeny analysis. However, the conventional Markov chain Monte Carlo algorithms, such as the Metropolis-Hastings algorithm, tend to get trapped in a local mode in simulating from the posterior distribution of phylogenetic trees, rendering the inference ineffective. In this paper, we apply an advanced Monte Carlo algorithm, the stochastic approximation Monte Carlo algorithm, to Bayesian phylogeny analysis. Our method is compared with two popular Bayesian phylogeny software, BAMBE and MrBayes, on simulated and real datasets. The numerical results indicate that our method outperforms BAMBE and MrBayes. Among the three methods, SAMC produces the consensus trees which have the highest similarity to the true trees, and the model parameter estimates which have the smallest mean square errors, but costs the least CPU time. © 2009 Elsevier Inc. All rights reserved.
Citation:
Cheon S, Liang F (2009) Bayesian phylogeny analysis via stochastic approximation Monte Carlo. Molecular Phylogenetics and Evolution 53: 394–403. Available: http://dx.doi.org/10.1016/j.ympev.2009.06.019.
Publisher:
Elsevier BV
Journal:
Molecular Phylogenetics and Evolution
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Nov-2009
DOI:
10.1016/j.ympev.2009.06.019
PubMed ID:
19589389
Type:
Article
ISSN:
1055-7903
Sponsors:
Liang's research was partially supported by the grant (DMS-0607755) made by the National Science Foundation and the award (KUS-C1-016-04) made by King Abdullah University of Science and Technology (KAUST). The authors thank the editor Professor A. L. Hughes and the referees for their comments which have led to significant improvement of this paper.
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Full metadata record

DC FieldValue Language
dc.contributor.authorCheon, Sooyoungen
dc.contributor.authorLiang, Famingen
dc.date.accessioned2016-02-25T12:43:50Zen
dc.date.available2016-02-25T12:43:50Zen
dc.date.issued2009-11en
dc.identifier.citationCheon S, Liang F (2009) Bayesian phylogeny analysis via stochastic approximation Monte Carlo. Molecular Phylogenetics and Evolution 53: 394–403. Available: http://dx.doi.org/10.1016/j.ympev.2009.06.019.en
dc.identifier.issn1055-7903en
dc.identifier.pmid19589389en
dc.identifier.doi10.1016/j.ympev.2009.06.019en
dc.identifier.urihttp://hdl.handle.net/10754/597656en
dc.description.abstractMonte Carlo methods have received much attention in the recent literature of phylogeny analysis. However, the conventional Markov chain Monte Carlo algorithms, such as the Metropolis-Hastings algorithm, tend to get trapped in a local mode in simulating from the posterior distribution of phylogenetic trees, rendering the inference ineffective. In this paper, we apply an advanced Monte Carlo algorithm, the stochastic approximation Monte Carlo algorithm, to Bayesian phylogeny analysis. Our method is compared with two popular Bayesian phylogeny software, BAMBE and MrBayes, on simulated and real datasets. The numerical results indicate that our method outperforms BAMBE and MrBayes. Among the three methods, SAMC produces the consensus trees which have the highest similarity to the true trees, and the model parameter estimates which have the smallest mean square errors, but costs the least CPU time. © 2009 Elsevier Inc. All rights reserved.en
dc.description.sponsorshipLiang's research was partially supported by the grant (DMS-0607755) made by the National Science Foundation and the award (KUS-C1-016-04) made by King Abdullah University of Science and Technology (KAUST). The authors thank the editor Professor A. L. Hughes and the referees for their comments which have led to significant improvement of this paper.en
dc.publisherElsevier BVen
dc.subjectBayesian phylogeny analysisen
dc.subjectConsensus treeen
dc.subjectMarkov chain Monte Carloen
dc.subjectStochastic approximation Monte Carloen
dc.titleBayesian phylogeny analysis via stochastic approximation Monte Carloen
dc.typeArticleen
dc.identifier.journalMolecular Phylogenetics and Evolutionen
dc.contributor.institutionKorea University, Seoul, South Koreaen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
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