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A Practical Guide to Phylogenetics for Nonexperts
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Published on: February 5, 2014

Robustness of phylogenetic inference based on minimum evolution.

Fabio Pardi1, Sylvain Guillemot, Olivier Gascuel

  • 1Méthodes et Algorithmes pour la Bioinformatique, LIRMM, CNRS-Université de Montpellier, 161 rue Ada, 34392 Montpellier, France. pardi@lirmm.fr

Bulletin of Mathematical Biology
|May 8, 2010
PubMed
Summary
This summary is machine-generated.

The study introduces the safety radius to measure the robustness of phylogenetic tree reconstruction. Balanced Minimum Evolution (BME) shows higher robustness than Ordinary Least Squares + Minimum Evolution (OLS + ME).

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Evolutionary Biology

Background:

  • Minimum evolution is a key principle in distance-based phylogeny reconstruction.
  • Neighbor-joining (NJ) is a widely used algorithm based on this principle.
  • Robustness to imprecise distance estimates is crucial for accurate tree inference.

Purpose of the Study:

  • To investigate the robustness of phylogenetic reconstruction methods to imprecise distance estimates.
  • To derive and compare the safety radius for Balanced Minimum Evolution (BME) and Ordinary Least Squares + Minimum Evolution (OLS + ME).
  • To explain observed differences in reconstruction accuracy between these methods.

Main Methods:

  • Derivation of the safety radius for BME and OLS + ME criteria.
  • Analysis of the safety radius's behavior with increasing numbers of taxa.
  • Comparison of derived safety radii to assess method robustness.

Main Results:

  • Balanced Minimum Evolution (BME) has a safety radius of 1/2, the optimal value.
  • Ordinary Least Squares + Minimum Evolution (OLS + ME) has a safety radius that approaches 0 as the number of taxa increases.
  • A significant difference in safety radii was found between BME and OLS + ME.

Conclusions:

  • BME offers superior robustness in phylogenetic reconstruction compared to OLS + ME.
  • The derived safety radii help explain the practical performance differences between BME and OLS + ME-based algorithms like NJ and FastME.
  • This study provides insights into the reliability of different tree inference methods.