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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Phylogenetic inference via sequential Monte Carlo.

Alexandre Bouchard-Côté¹, Sriram Sankararaman, Michael I Jordan

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

Systematic Biology

|January 7, 2012

Summary

This summary is machine-generated.

We introduce PosetSMC, a novel Sequential Monte Carlo (SMC) method for phylogenetic analysis. PosetSMC offers a faster, more scalable alternative to Markov chain Monte Carlo (MCMC) for complex evolutionary modeling.

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Area of Science:

Computational Biology
Evolutionary Biology
Statistical Phylogenetics

Background:

Bayesian inference is a powerful framework for phylogenetic analysis, handling complex models and uncertainty.
Current Markov chain Monte Carlo (MCMC) methods face scalability challenges with large phylogenetic datasets.
This limits the widespread adoption of Bayesian approaches in phylogenetics.

Purpose of the Study:

To present Sequential Monte Carlo (SMC) as a viable alternative to MCMC for phylogenetic inference.
To introduce PosetSMC, an extension of SMC utilizing partially ordered sets for phylogenetic analysis.
To evaluate the performance and advantages of PosetSMC compared to existing MCMC methods.

Main Methods:

Developed an extension of Sequential Monte Carlo (SMC) using partially ordered sets, termed PosetSMC.
Applied the PosetSMC framework to phylogenetic analysis.
Conducted theoretical analysis and experimental evaluations on synthetic and real-world phylogenetic data.

Main Results:

PosetSMC demonstrated significantly faster convergence, up to two orders of magnitude improvement over MCMC.
The method proved effective on both synthetic and real phylogenetic datasets.
PosetSMC facilitates marginal likelihood estimation and parallel/distributed computing.

Conclusions:

PosetSMC represents a promising, computationally efficient alternative to MCMC for Bayesian phylogenetic analysis.
Its scalability and features support broader adoption of Bayesian methods in phylogenetics.
PosetSMC can be integrated with MCMC in hybrid schemes for enhanced performance.