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A new fast method for inferring multiple consensus trees using k-medoids.

Nadia Tahiri1, Matthieu Willems1, Vladimir Makarenkov2

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Summary
This summary is machine-generated.

This study introduces a fast method for inferring multiple consensus trees from gene trees, improving the identification of distinct evolutionary histories within large genomic datasets.

Keywords:
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Area of Science:

  • Phylogenetics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Gene trees provide insights into gene family evolution but inferring a single species consensus tree can obscure specific evolutionary patterns.
  • Different evolutionary histories, due to horizontal gene transfer or ancient duplications, complicate the inference of a reliable species consensus tree.
  • Existing methods for inferring consensus trees from multiple gene trees may result in the loss of crucial evolutionary information.

Purpose of the Study:

  • To develop a novel, efficient method for inferring multiple consensus trees from a set of phylogenetic trees.
  • To address the limitations of single consensus tree inference methods that can obscure specific evolutionary patterns.
  • To enable the identification of distinct evolutionary histories within gene family datasets.

Main Methods:

  • A new fast method for inferring multiple consensus trees from sets of phylogenetic trees (additive trees or X-trees).
  • Utilizes the k-medoids partitioning algorithm to cluster trees into groups.
  • Incorporates novel versions of Silhouette and Caliński-Harabasz cluster validity indices adapted for tree clustering.

Main Results:

  • Successfully infers multiple consensus trees from a given set of gene trees.
  • Identifies groups of gene trees with similar intragroup and different intergroup evolutionary histories.
  • Demonstrated efficiency on synthetic and real phylogenetic datasets, including archaeal organisms.

Conclusions:

  • The developed method effectively infers multiple consensus trees, revealing distinct evolutionary patterns.
  • Offers a significant speed advantage over existing tree clustering approaches.
  • Suitable for analyzing large genomic and phylogenetic datasets due to its speed and accuracy.