Parallel Metropolis Coupled Markov Chain Monte Carlo for Isolation with Migration Model

Isolation with Migration model (IM), which jointly estimates divergence times and migration rates between two populations from DNA sequence data, can capture many phenomena when one population splits into two. The parameters inferences for IM are based on Markov Chain Monte Carlo method (MCMC). Standard implementations of MCMC are prone to fall into local optima. Metropolis Coupled MCMC ((MC) 3 ) as a variant of MCMC can more readily explore multiple peaks in posterior distribution of trees. Expensive execution time has limited the application of (MC) 3 . This paper proposes a Parallel Metropolis Coupled Markov Chain Monte Carlo for IM. The proposed parallel algorithm retains the ability of (MC) 3 and maintains a fast execution time. Performance results indicate nearly linear speed improvement. This paper provides researcher with rapider and more high-efficiency methods to study population genetics and molecular ecology problems aided with super computer.

[1]  X. Feng,et al.  PBPI: a High Performance Implementation of Bayesian Phylogenetic Inference , 2006, ACM/IEEE SC 2006 Conference (SC'06).

[2]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[3]  Asami,et al.  Assessing the Cretaceous Superordinal Divergence Times within Birds and Placental Mammals by Using Whole Mitochondrial Protein Sequences and an Extended Statistical Framework , 2001 .

[4]  Jilin Zhang,et al.  OpenMP compiler for distributed memory architectures , 2010, Science China Information Sciences.

[5]  Byungjoo Kim,et al.  GPU Accelerated Molecular Surface Computing , 2012 .

[6]  M A Newton,et al.  Bayesian Phylogenetic Inference via Markov Chain Monte Carlo Methods , 1999, Biometrics.

[7]  C. Geyer.  Markov Chain Monte Carlo Maximum Likelihood , 1991 .

[8]  Manami Sugimoto,et al.  Genetic population structure of the mudskipper, Periophthalmus modestus, in Japan inferred from mitochondrial DNA sequence variations. , 2006 .

[9]  William Gropp,et al.  Skjellum using mpi: portable parallel programming with the message-passing interface , 1994 .

[10]  B. Larget,et al.  Markov Chain Monte Carlo Algorithms for the Bayesian Analysis of Phylogenetic Trees , 2000 .

[11]  J. Hey.  On the Number of New World Founders: A Population Genetic Portrait of the Peopling of the Americas , 2005, PLoS biology.

[12]  Anthony Skjellum,et al.  Using MPI - portable parallel programming with the message-parsing interface , 1994 .

[13]  Jody Hey,et al.  Divergence population genetics of chimpanzees. , 2004, Molecular biology and evolution.

[14]  Jilin Zhang,et al.  Message scheduling for array re-decomposition on distributed memory systems , 2010, Future Gener. Comput. Syst..

[15]  Hani Doss,et al.  Phylogenetic Tree Construction Using Markov Chain Monte Carlo , 2000 .

[16]  R. Nielsen,et al.  Multilocus Methods for Estimating Population Sizes, Migration Rates and Divergence Time, With Applications to the Divergence of Drosophila pseudoobscura and D. persimilis , 2004, Genetics.

[17]  Sandhya Dwarkadas,et al.  Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference , 2002, Bioinform..

[18]  Radford M. Neal.  Probabilistic Inference Using Markov Chain Monte Carlo Methods , 2011 .

[19]  B. Rannala,et al.  Bayesian phylogenetic inference using DNA sequences: a Markov Chain Monte Carlo Method. , 1997, Molecular biology and evolution.

[20]  R. Nielsen,et al.  Distinguishing migration from isolation: a Markov chain Monte Carlo approach. , 2001, Genetics.

[21]  Wonil Kim,et al.  A Distributed Hybrid Algorithm for Composite Stock Cutting , 2012 .

[22]  John P. Huelsenbeck,et al.  MRBAYES: Bayesian inference of phylogenetic trees , 2001, Bioinform..