Building alternative consensus trees and supertrees using k -means and Robinson and Foulds distance

  1. Nadia Tahiri
  2. Bernard Fichet
  3. Vladimir Makarenkov

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Abstract

Motivation

Each gene has its own evolutionary history which can substantially differ from evolutionary histories of other genes. For example, some individual genes or operons can be affected by specific horizontal gene transfer or recombination events. Thus, the evolutionary history of each gene should be represented by its own phylogenetic tree which may display different evolutionary patterns from the species tree that accounts for the main patterns of vertical descent. However, the output of traditional consensus tree or supertree inference methods is a unique consensus tree or supertree.

Results

We present a new efficient method for inferring multiple alternative consensus trees and supertrees to best represent the most important evolutionary patterns of a given set of gene phylogenies. We show how an adapted version of the popular k-means clustering algorithm, based on some remarkable properties of the Robinson and Foulds distance, can be used to partition a given set of trees into one (for homogeneous data) or multiple (for heterogeneous data) cluster(s) of trees. Moreover, we adapt the popular Caliński–Harabasz, Silhouette, Ball and Hall, and Gap cluster validity indices to tree clustering with k-means. Special attention is given to the relevant but very challenging problem of inferring alternative supertrees. The use of the Euclidean property of the objective function of the method makes it faster than the existing tree clustering techniques, and thus better suited for analyzing large evolutionary datasets.

Availability and implementation

Our KMeansSuperTreeClustering program along with its C++ source code is available at: https://github.com/TahiriNadia/KMeansSuperTreeClustering.

Supplementary information

Supplementary data are available at Bioinformatics online.

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  1. Version published to 10.1093/bioinformatics/btac326
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    SciScore for 10.1101/2021.03.24.436812: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

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    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code.

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  3. Version published to 10.1101/2021.03.24.436812v1 on bioRxiv