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Phylogeny01:23

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Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.
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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Particle Gibbs sampling for Bayesian phylogenetic inference.

Shijia Wang1, Liangliang Wang2

  • 1School of Statistic and Data Science, LPMC and KLMDASR, Nankai University, Nankai Qu 300071, China.

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|October 12, 2020
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Summary
This summary is machine-generated.

We developed a novel combinatorial sequential Monte Carlo (CSMC) method to improve Bayesian phylogenetic tree inference. This new approach enhances efficiency in particle Gibbs samplers for evolutionary models.

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Statistical Modeling

Background:

  • Combinatorial sequential Monte Carlo (CSMC) and Markov chain Monte Carlo (MCMC) are used for Bayesian phylogenetic tree inference.
  • Combining CSMC and MCMC within particle Gibbs (PG) samplers is desirable for joint estimation of phylogenetic trees and evolutionary parameters.
  • Standard PG samplers can exhibit poor mixing in high-dimensional problems like phylogenetic tree inference, with existing remedies being inefficient for the combinatorial tree space.

Purpose of the Study:

  • To introduce a novel CSMC method with an improved proposal distribution for more efficient phylogenetic tree inference.
  • To integrate this new CSMC method into the particle Gibbs (PG) sampler framework for inferring parameters in evolutionary models.
  • To develop a parallelizable algorithm for enhanced computational efficiency.

Main Methods:

  • Development of a novel combinatorial sequential Monte Carlo (CSMC) algorithm.
  • Proposal of a more efficient proposal distribution within the CSMC framework.
  • Integration of the novel CSMC into the particle Gibbs (PG) sampler.
  • Parallelization of the CSMC algorithm across multiple computing cores.

Main Results:

  • The developed CSMC method demonstrates more efficient tree sampling within various PG samplers.
  • Numerical experiments validate the improved efficiency of the novel CSMC algorithm.
  • The algorithm is easily parallelizable, allowing for efficient computation on multi-core systems.

Conclusions:

  • The novel CSMC method offers a significant improvement for Bayesian phylogenetic tree inference.
  • The enhanced PG sampler framework incorporating the new CSMC is efficient for evolutionary model parameter estimation.
  • The method's parallelizability contributes to its practical utility in computational phylogenetics.