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Constructing rooted supertrees using distances.

Stephen J Willson1

  • 1Department of Mathematics, Iowa State University, Ames, IA 50011, USA. swillson@iastate.edu

Bulletin of Mathematical Biology
|November 4, 2004
PubMed
Summary

This study introduces BUILD-WITH-DISTANCES, a novel method for constructing phylogenetic supertrees using distance information. The approach enhances resolution compared to topology-only methods, even with incompatible input trees.

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Bioinformatics

Background:

  • Phylogenetic trees represent evolutionary relationships among species.
  • Constructing a single supertree from multiple phylogenetic trees is a complex challenge.
  • Existing methods often rely solely on tree topology, potentially limiting resolution.

Purpose of the Study:

  • To develop a polynomial-time method for constructing rooted phylogenetic supertrees.
  • To incorporate distance information from input trees to improve supertree resolution.
  • To provide a method that handles incompatible input trees, producing trees with provable properties.

Main Methods:

  • The study proposes the BUILD-WITH-DISTANCES algorithm, a novel method for supertree construction.
  • This method utilizes distance information inherent in the input phylogenetic trees.
  • Variants of the method are presented to address cases of incompatible input trees.

Main Results:

  • The BUILD-WITH-DISTANCES method constructs a rooted tree (S0) that serves as a supertree when one exists.
  • The resulting supertree (S0) often exhibits higher resolution than trees generated by topology-only methods.
  • The method produces trees with provable properties even when input trees are incompatible.

Conclusions:

  • The BUILD-WITH-DISTANCES method offers an efficient and effective approach to phylogenetic supertree construction.
  • Incorporating distance information significantly enhances the resolution of phylogenetic supertrees.
  • The method provides a robust solution for inferring evolutionary relationships from diverse and potentially conflicting datasets.

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