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A Practical Guide to Phylogenetics for Nonexperts
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Geodesics to characterize the phylogenetic landscape.

Marzieh Khodaei1, Megan Owen2, Peter Beerli1

  • 1Department of Scientific Computing, Florida State University, Tallahassee, FL, United States of America.

Plos One
|June 23, 2023
PubMed
Summary
This summary is machine-generated.

We introduce pathtrees, a novel method for exploring phylogenetic treespace by generating intermediate trees. This approach aids in visualizing evolutionary relationships and discovering new, highly likely phylogenetic trees.

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Area of Science:

  • Evolutionary biology
  • Computational phylogenetics
  • Bioinformatics

Background:

  • Phylogenetic trees are crucial for evolutionary history, but identifying maximum likelihood trees is computationally intensive due to complex likelihood landscapes and vast tree spaces.
  • Existing tree search algorithms often explore limited regions of the treespace, potentially missing optimal or novel phylogenetic hypotheses.

Purpose of the Study:

  • To develop a method for generating and visualizing intermediate phylogenetic trees (pathtrees) on the shortest paths between any two trees using the Billera-Holmes-Vogtmann (BHV) distance.
  • To explore under-investigated regions of the phylogenetic treespace, identify locally optimal trees, and discover high-likelihood trees potentially missed by current algorithms.
  • To compare the performance of the pathtree method against established tree search tools like Paup*, RAxML, and RevBayes.

Main Methods:

  • The Billera-Holmes-Vogtmann (BHV) distance was used to define shortest paths between phylogenetic trees.
  • Intermediate trees, termed pathtrees, were generated along these shortest paths.
  • The method was applied to two datasets: primate (23 species) and milksnake (182 individuals) mitochondrial DNA sequences.
  • Phylogenetic treespace was visualized using log-likelihood as a fitness function.
  • Performance was evaluated by comparing the highest likelihood trees found and the novelty of topologies against Paup*, RAxML, and RevBayes, including MCMC methods.
  • The accuracy of treespace mapping was validated using dimensionality reduction techniques.

Main Results:

  • The pathtree method successfully generated intermediate trees, providing a structured way to explore and visualize portions of the phylogenetic treespace.
  • The approach identified trees with likelihoods comparable to those found by heuristic search methods and revealed novel topologies.
  • Visualization of the likelihood landscape at different scales was achieved, highlighting potential likelihood terraces.
  • The method discovered relevant phylogenetic trees not identified by Markov Chain Monte Carlo (MCMC) methods.
  • Validation metrics confirmed the method's effectiveness in mapping treespace into lower dimensions.

Conclusions:

  • Pathtrees offer a complementary approach to heuristic search analyses in phylogenetics.
  • The visualization capabilities of this method enable detailed inspection of likelihood landscapes and exploration of previously unvisited treespace regions.
  • This method enhances the discovery of optimal and novel phylogenetic hypotheses by systematically exploring the tree space.