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Inferring Multiple Consensus Trees and Supertrees Using Clustering: A Review

In: Data Analysis and Optimization

Author

Listed:
  • Vladimir Makarenkov

    (Université du Québec à Montréal)

  • Gayane S. Barseghyan

    (Université du Québec à Montréal)

  • Nadia Tahiri

    (Université de Sherbrooke)

Abstract

Phylogenetic trees (i.e. evolutionary trees, additive trees or X-trees) play a key role in the processes of modeling and representing species evolution. Genome evolution of a given group of species is usually modeled by a species phylogenetic tree that represents the main patterns of vertical descent. However, the evolution of each gene is unique. It can be represented by its own gene tree which can differ substantially from a general species tree representation. Consensus trees and supertrees have been widely used in evolutionary studies to combine phylogenetic information contained in individual gene trees. Nevertheless, if the available gene trees are quite different, then the resulting consensus tree or supertree can either include many unresolved subtrees corresponding to internal nodes of high degree or can simply be a star tree. This may happen if the available gene trees have been affected by different reticulate evolutionary events, such as horizontal gene transfer, hybridization or genetic recombination. In this case, the problem of inferring multiple alternative consensus trees or supertrees, using clustering, becomes relevant since it allows one to regroup in different cluster gene trees having similar evolutionary patterns (e.g. gene trees representing genes that have undergone the same horizontal gene transfer or recombination events). We critically review recent advances and methods in the field of phylogenetic tree clustering, discuss the methods’ mathematical properties, and describe the advantages and limitations of multiple consensus tree and supertree approaches. In the application section, we show how the discussed supertree clustering approach can be used to cluster aaRS evolutionary trees according to their evolutionary patterns.

Suggested Citation

  • Vladimir Makarenkov & Gayane S. Barseghyan & Nadia Tahiri, 2023. "Inferring Multiple Consensus Trees and Supertrees Using Clustering: A Review," Springer Optimization and Its Applications, in: Boris Goldengorin & Sergei Kuznetsov (ed.), Data Analysis and Optimization, pages 191-213, Springer.
  • Handle: RePEc:spr:spochp:978-3-031-31654-8_13
    DOI: 10.1007/978-3-031-31654-8_13
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