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Related Experiment Videos

Influence function for robust phylogenetic reconstructions.

Avner Bar-Hen1, Mahendra Mariadassou, Marie-Anne Poursat

  • 1Univ. René Descartes, MAP5, 45 rue des Saints-Pères, 75270 Paris, France. avner@math-info.univ-paris5.fr

Molecular Biology and Evolution
|February 12, 2008
PubMed
Summary
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Influence functions identify outlier data in phylogenetic analyses, significantly improving tree accuracy. Removing these outliers enhances bootstrap support and revises fungal evolutionary hypotheses.

Area of Science:

  • Phylogenetics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Maximum-likelihood (ML) phylogenetic reconstructions are sensitive to data quality.
  • Identifying and handling influential data points is crucial for robust evolutionary inference.
  • Existing methods may not adequately address the impact of individual data sites on tree topology.

Purpose of the Study:

  • To introduce and apply the influence function for analyzing data support in ML phylogenetic trees.
  • To develop a novel data filtering tool for phylogenetic reconstructions.
  • To investigate the impact of outlier sites on fungal phylogenetics.

Main Methods:

  • Computation of the influence function to assess the impact of each data site on parameter inference.

Related Experiment Videos

  • Analysis of ML phylogenetic trees for individual sites within a fungal dataset.
  • Identification and removal of outlier sites with highly negative influence values.
  • Reconstruction of ML trees using filtered datasets.

    • Main Results:

    • Outlier sites can drastically alter phylogenetic tree topology.
    • Removing the strongest outlier significantly modified the ML topology, reducing internal nodes by 20%.
    • Filtered datasets yielded ML topologies with enhanced bootstrap support.
    • The polyphyletic nature of Chytridiomycota and Zygomycota was reinforced.

    Conclusions:

    • Influence functions are valuable for identifying problematic data in phylogenetic analyses.
    • Data filtering based on influence values improves the reliability of phylogenetic inference.
    • The study necessitates a re-evaluation of the systematics of Chytridiomycota and Zygomycota.
    • Influence functions can generate novel hypotheses regarding evolutionary processes.