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Phylogenetic inference via sequential Monte Carlo.

Bouchard-Côté A, Sankararaman S, Jordan MI - Syst. Biol. (2012)

Bottom Line: We provide a theoretical treatment of PosetSMC and also present experimental evaluation of PosetSMC on both synthetic and real data.The empirical results demonstrate that PosetSMC is a very promising alternative to MCMC, providing up to two orders of magnitude faster convergence.We discuss other factors favorable to the adoption of PosetSMC in phylogenetics, including its ability to estimate marginal likelihoods, its ready implementability on parallel and distributed computing platforms, and the possibility of combining with MCMC in hybrid MCMC-SMC schemes.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z2, Canada.

ABSTRACT
Bayesian inference provides an appealing general framework for phylogenetic analysis, able to incorporate a wide variety of modeling assumptions and to provide a coherent treatment of uncertainty. Existing computational approaches to bayesian inference based on Markov chain Monte Carlo (MCMC) have not, however, kept pace with the scale of the data analysis problems in phylogenetics, and this has hindered the adoption of bayesian methods. In this paper, we present an alternative to MCMC based on Sequential Monte Carlo (SMC). We develop an extension of classical SMC based on partially ordered sets and show how to apply this framework--which we refer to as PosetSMC--to phylogenetic analysis. We provide a theoretical treatment of PosetSMC and also present experimental evaluation of PosetSMC on both synthetic and real data. The empirical results demonstrate that PosetSMC is a very promising alternative to MCMC, providing up to two orders of magnitude faster convergence. We discuss other factors favorable to the adoption of PosetSMC in phylogenetics, including its ability to estimate marginal likelihoods, its ready implementability on parallel and distributed computing platforms, and the possibility of combining with MCMC in hybrid MCMC-SMC schemes. Software for PosetSMC is available at http://www.stat.ubc.ca/ bouchard/PosetSMC.

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Comparison of the convergence time of PosetSMC and MCMC. We generated coalescent trees of different sizes and data sets of 1000 nucleotides. We computed the L1 distance of the minimum Bayes risk reconstruction to the true generating tree as a function of the running time (in units of the number of peeling recursions, on a log scale). The missing MCMC data points are due to MrBayes stalling on these executions.
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fig3: Comparison of the convergence time of PosetSMC and MCMC. We generated coalescent trees of different sizes and data sets of 1000 nucleotides. We computed the L1 distance of the minimum Bayes risk reconstruction to the true generating tree as a function of the running time (in units of the number of peeling recursions, on a log scale). The missing MCMC data points are due to MrBayes stalling on these executions.

Mentions: We computed consensus trees from the samples and measured the distance of this reconstruction to the generating tree (using the metrics defined in the Background and Notation section). The results are shown in Figure 3 for the L1 metric. For each algorithm setting and tree size, we show the median distance across 100 executions, as well as the first and third quartiles. A speedup of over two orders of magnitudes can be seen consistently across these experiments.FIGURE 3.


Phylogenetic inference via sequential Monte Carlo.

Bouchard-Côté A, Sankararaman S, Jordan MI - Syst. Biol. (2012)

Comparison of the convergence time of PosetSMC and MCMC. We generated coalescent trees of different sizes and data sets of 1000 nucleotides. We computed the L1 distance of the minimum Bayes risk reconstruction to the true generating tree as a function of the running time (in units of the number of peeling recursions, on a log scale). The missing MCMC data points are due to MrBayes stalling on these executions.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376373&req=5

fig3: Comparison of the convergence time of PosetSMC and MCMC. We generated coalescent trees of different sizes and data sets of 1000 nucleotides. We computed the L1 distance of the minimum Bayes risk reconstruction to the true generating tree as a function of the running time (in units of the number of peeling recursions, on a log scale). The missing MCMC data points are due to MrBayes stalling on these executions.
Mentions: We computed consensus trees from the samples and measured the distance of this reconstruction to the generating tree (using the metrics defined in the Background and Notation section). The results are shown in Figure 3 for the L1 metric. For each algorithm setting and tree size, we show the median distance across 100 executions, as well as the first and third quartiles. A speedup of over two orders of magnitudes can be seen consistently across these experiments.FIGURE 3.

Bottom Line: We provide a theoretical treatment of PosetSMC and also present experimental evaluation of PosetSMC on both synthetic and real data.The empirical results demonstrate that PosetSMC is a very promising alternative to MCMC, providing up to two orders of magnitude faster convergence.We discuss other factors favorable to the adoption of PosetSMC in phylogenetics, including its ability to estimate marginal likelihoods, its ready implementability on parallel and distributed computing platforms, and the possibility of combining with MCMC in hybrid MCMC-SMC schemes.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z2, Canada.

ABSTRACT
Bayesian inference provides an appealing general framework for phylogenetic analysis, able to incorporate a wide variety of modeling assumptions and to provide a coherent treatment of uncertainty. Existing computational approaches to bayesian inference based on Markov chain Monte Carlo (MCMC) have not, however, kept pace with the scale of the data analysis problems in phylogenetics, and this has hindered the adoption of bayesian methods. In this paper, we present an alternative to MCMC based on Sequential Monte Carlo (SMC). We develop an extension of classical SMC based on partially ordered sets and show how to apply this framework--which we refer to as PosetSMC--to phylogenetic analysis. We provide a theoretical treatment of PosetSMC and also present experimental evaluation of PosetSMC on both synthetic and real data. The empirical results demonstrate that PosetSMC is a very promising alternative to MCMC, providing up to two orders of magnitude faster convergence. We discuss other factors favorable to the adoption of PosetSMC in phylogenetics, including its ability to estimate marginal likelihoods, its ready implementability on parallel and distributed computing platforms, and the possibility of combining with MCMC in hybrid MCMC-SMC schemes. Software for PosetSMC is available at http://www.stat.ubc.ca/ bouchard/PosetSMC.

Show MeSH
Related in: MedlinePlus