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Online Bayesian Phylogenetic Inference: Theoretical Foundations via Sequential Monte Carlo.

Vu Dinh1, Aaron E Darling2, Frederick A Matsen Iv3

  • 1Department of Mathematical Sciences, University of Delaware, 312 Ewing Hall, Newark, DE 19716, USA.

Systematic Biology
|December 16, 2017
PubMed
Summary

This study introduces an efficient online Bayesian phylogenetics method using Sequential Monte Carlo (SMC) to update evolutionary trees with new DNA sequences. The method ensures accurate tree inference and scales effectively with growing datasets.

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Bioinformatics

Background:

  • Phylogenetics infers evolutionary history from molecular data, with Bayesian methods being popular but computationally intensive.
  • Current Bayesian phylogenetic methods require re-computation for each new sequence, hindering efficient updates.
  • The rapid growth of sequence databases necessitates methods for updating phylogenetic estimates without starting from scratch.

Purpose of the Study:

  • To develop and theoretically validate an online Bayesian phylogenetic method for updating existing evolutionary tree estimates with new sequence data.
  • To address the computational cost of maintaining up-to-date phylogenetic analyses in the face of accumulating sequence data.

Main Methods:

  • Theoretical analysis of online Bayesian phylogenetic inference using Sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC).
  • Demonstration of consistency for the SMC-based method in the limit of a large number of particles.
  • Derivation of bounds on phylogenetic likelihood surface changes upon sequence addition.

Main Results:

  • The proposed online method is theoretically consistent, sampling from the correct posterior distribution.
  • Novel bounds characterize how phylogenetic likelihood surfaces change with new sequences.
  • The effective sample size (ESS) of the SMC sampler is shown to grow linearly with the number of particles, even as model dimensions increase.

Conclusions:

  • Online Bayesian phylogenetics using SMC offers a computationally efficient solution for updating evolutionary trees with new data.
  • The theoretical guarantees of consistency and linear ESS growth validate the method's performance and scalability.
  • This approach is crucial for managing and analyzing the ever-expanding volume of molecular sequence data in evolutionary studies.