Clustering Genes of Common Evolutionary History.

Détails

ID Serval
serval:BIB_36DE5D65E7A5
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Clustering Genes of Common Evolutionary History.
Périodique
Molecular biology and evolution
Auteur(s)
Gori K., Suchan T., Alvarez N., Goldman N., Dessimoz C.
ISSN
1537-1719 (Electronic)
ISSN-L
0737-4038
Statut éditorial
Publié
Date de publication
06/2016
Peer-reviewed
Oui
Volume
33
Numéro
6
Pages
1590-1605
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: ppublish
Résumé
Phylogenetic inference can potentially result in a more accurate tree using data from multiple loci. However, if the loci are incongruent-due to events such as incomplete lineage sorting or horizontal gene transfer-it can be misleading to infer a single tree. To address this, many previous contributions have taken a mechanistic approach, by modeling specific processes. Alternatively, one can cluster loci without assuming how these incongruencies might arise. Such "process-agnostic" approaches typically infer a tree for each locus and cluster these. There are, however, many possible combinations of tree distance and clustering methods; their comparative performance in the context of tree incongruence is largely unknown. Furthermore, because standard model selection criteria such as AIC cannot be applied to problems with a variable number of topologies, the issue of inferring the optimal number of clusters is poorly understood. Here, we perform a large-scale simulation study of phylogenetic distances and clustering methods to infer loci of common evolutionary history. We observe that the best-performing combinations are distances accounting for branch lengths followed by spectral clustering or Ward's method. We also introduce two statistical tests to infer the optimal number of clusters and show that they strongly outperform the silhouette criterion, a general-purpose heuristic. We illustrate the usefulness of the approach by 1) identifying errors in a previous phylogenetic analysis of yeast species and 2) identifying topological incongruence among newly sequenced loci of the globeflower fly genus Chiastocheta We release treeCl, a new program to cluster genes of common evolutionary history (http://git.io/treeCl).

Mots-clé
Base Sequence, Biological Evolution, Cluster Analysis, Computer Simulation, Models, Genetic, Multigene Family, Phylogeny, Sequence Analysis, DNA/methods, Software, Yeasts/genetics, clustering, incomplete lineage sorting., incongruence, nonorthology, phylogeny, process-agnostic
Pubmed
Web of science
Open Access
Oui
Création de la notice
16/02/2016 17:24
Dernière modification de la notice
20/08/2019 13:25
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