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Related Experiment Videos

Probabilistic Distances Between Trees.

Maryam K Garba1,2, Tom M W Nye1, Richard J Boys1

  • 1School of Mathematics & Statistics, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.

Systematic Biology
|October 14, 2017
PubMed
Summary
This summary is machine-generated.

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We introduce novel phylogenetic tree distance measures based on probability distributions, not just tree structure. These methods efficiently approximate distances and incorporate evolutionary model parameters for robust phylogenetic analysis.

Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Phylogenetic tree comparison traditionally relies on tree geometry or topology.
  • Existing methods may not fully capture evolutionary processes or handle diverse taxon sets effectively.

Purpose of the Study:

  • To develop and evaluate new phylogenetic tree distance measures based on probability distributions.
  • To incorporate substitution model parameters and accommodate trees with different taxon sets.
  • To compare these novel distances with existing metrics using multidimensional scaling.

Main Methods:

  • Utilizing probability distributions on genetic sequence data induced by phylogenetic trees.
  • Employing efficient Monte Carlo sampling schemes for approximate distance calculation.

Related Experiment Videos

  • Applying multidimensional scaling (MDS) for data visualization and comparison.

    • Main Results:

    • Demonstrated the properties of the new probability-distribution-based distance measures.
    • Showcased the ability to include substitution model parameters and handle varying taxon sets.
    • Identified phylogenetic islands in previously analyzed datasets using the new metrics.

    Conclusions:

    • The novel distance measures offer a principled approach to comparing phylogenetic trees based on underlying data distributions.
    • These methods provide a more nuanced understanding of tree relationships, especially when incorporating evolutionary parameters.
    • The approach is effective for exploring complex datasets and identifying potential phylogenetic artifacts.