Clustering Genes of Common Evolutionary History.

Details

Serval ID
serval:BIB_36DE5D65E7A5
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Clustering Genes of Common Evolutionary History.
Journal
Molecular biology and evolution
Author(s)
Gori K., Suchan T., Alvarez N., Goldman N., Dessimoz C.
ISSN
1537-1719 (Electronic)
ISSN-L
0737-4038
Publication state
Published
Issued date
06/2016
Peer-reviewed
Oui
Volume
33
Number
6
Pages
1590-1605
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
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).

Keywords
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
Yes
Create date
16/02/2016 18:24
Last modification date
20/08/2019 14:25
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