Inferring Multiple Consensus Trees and Supertrees Using Clustering: A Review

Vladimir Makarenkov (), Gayane S. Barseghyan and Nadia Tahiri
Additional contact information
Vladimir Makarenkov: Université du Québec à Montréal
Gayane S. Barseghyan: Université du Québec à Montréal
Nadia Tahiri: Université de Sherbrooke

A chapter in Data Analysis and Optimization, 2023, pp 191-213 from Springer

Abstract: 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.

Keywords: Clustering; Cluster validity index; Consensus tree; K-means; K-medoids; Phylogenetic tree; Robinson and Foulds distance; Supertree (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-31654-8_13

Ordering information: This item can be ordered from
http://www.springer.com/9783031316548

DOI: 10.1007/978-3-031-31654-8_13

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().