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Related Concept Videos

Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...
Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...
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Understanding the evolutionary relationships among microorganisms is fundamental to microbial ecology and taxonomy. Phylogenetic trees are essential tools for inferring these relationships, relying primarily on comparative analyses of molecular sequences such as DNA, RNA, or proteins. In microbial studies, these trees typically depict the evolutionary paths of diverse bacterial and archaeal species by mapping genetic differences accumulated over time.Phylogenetic trees are composed of tips,...
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Identifiability of Phylogenetic Parameters from k-mer Data Under the Coalescent.

Bulletin of mathematical biology·2018
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A Practical Guide to Phylogenetics for Nonexperts
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Published on: February 5, 2014

Phylogenetic Network Models as Graphical Models.

Seth Sullivant1

  • 1Department of Mathematics, North Carolina State University, Raleigh, NC, 27695, USA. smsulli2@ncsu.edu.

Bulletin of Mathematical Biology
|July 13, 2026
PubMed
Summary

This study introduces a new phylogenetic network model as a submodel of directed acyclic graphs (DAGs). It reveals nonidentifiability issues in displayed tree models and generalizes phylogenetic tree results using probability tensors.

Keywords:
Phylogenetic network modelsgraphical modelsidentifiability

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Area of Science:

  • * Computational Biology
  • * Phylogenetics
  • * Graph Theory

Background:

  • * Phylogenetic network models are crucial for understanding evolutionary relationships.
  • * Directed acyclic graphs (DAGs) provide a framework for representing complex dependencies.
  • * Existing models may not fully capture the nuances of reticulate evolution.

Purpose of the Study:

  • * To represent displayed tree phylogenetic network models as a submodel of DAG graphical models.
  • * To develop and apply the concept of local modifications to DAG models for phylogenetic networks.
  • * To identify and address nonidentifiability issues in displayed tree models, particularly concerning reticulation edges.

Main Methods:

  • * Representing phylogenetic network models within the graphical model framework of DAGs.
  • * Developing a novel concept of local modifications applicable to DAG models.
  • * Analyzing probability tensors and deriving rank conditions for displayed tree models.

Main Results:

  • * Demonstrated that displayed tree phylogenetic network models are natural submodels of DAG graphical models.
  • * Introduced local modifications for DAGs and applied them to displayed tree models.
  • * Highlighted nonidentifiability issues in displayed tree models, including stacked reticulations.
  • * Generalized rank conditions for probability tensors, extending classic phylogenetic tree results.

Conclusions:

  • * The DAG framework offers a powerful representation for displayed tree phylogenetic networks.
  • * Local modifications provide a tool for analyzing and understanding these networks.
  • * The study advances the theoretical understanding of phylogenetic network identifiability and tensor analysis.