Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference

Authors:
Gautam Altekar;Sandhya Dwarkadas;John P. Huelsenbeck;Fredrik Ronquist
Affiliations:
Department of Computer Science, University of Rochester,;Department of Computer Science, University of Rochester,;Section of Ecology, Behavior and Evolution, Division of Biological Sciences, University of California, San Diego;Department of Systematic Zoology, Evolutionary Biology Centre, Uppsala University
Venue:
Bioinformatics
Year:
2004

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Abstract

Motivation: Bayesian estimation of phylogeny is based on the posterior probability distribution of trees. Currently, the only numerical method that can effectively approximate posterior probabilities of trees is Markov chain Monte Carlo (MCMC). Standard implementations of MCMC can be prone to entrapment in local optima. Metropolis coupled MCMC [(MC)3], a variant of MCMC, allows multiple peaks in the landscape of trees to be more readily explored, but at the cost of increased execution time. Results: This paper presents a parallel algorithm for (MC)3. The proposed parallel algorithm retains the ability to explore multiple peaks in the posterior distribution of trees while maintaining a fast execution time. The algorithm has been implemented using two popular parallel programming models: message passing and shared memory. Performance results indicate nearly linear speed improvement in both programming models for small and large data sets. Availability: MrBayes v3.0 is available at http://morphbank.ebc.uu.se/mrbayes/