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Mol Biol Evol. 2016 Jun;33(6):1590-605. doi: 10.1093/molbev/msw038. Epub 2016 Feb 17.

Clustering Genes of Common Evolutionary History.

Author information

1
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Campus, Hinxton, United Kingdom.
2
Department of Ecology and Evolution, Biophore Building, UNIL-Sorge, University of Lausanne, Lausanne, Switzerland.
3
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Campus, Hinxton, United Kingdom goldman@ebi.ac.uk c.dessimoz@ucl.ac.uk.
4
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Campus, Hinxton, United Kingdom Department of Ecology and Evolution, Biophore Building, UNIL-Sorge, University of Lausanne, Lausanne, Switzerland Department of Genetics, Evolution & Environment, University College London, London, United Kingdom Department of Computer Science, University College London, London, United Kingdom Centre for Integrative Genomics, University of Lausanne, Lausanne, Switzerland Swiss Institute of Bioinformatics, Biophore, Lausanne, Switzerland goldman@ebi.ac.uk c.dessimoz@ucl.ac.uk.

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:

clustering; incomplete lineage sorting.; incongruence; nonorthology; phylogeny; process-agnostic

PMID:
26893301
PMCID:
PMC4868114
DOI:
10.1093/molbev/msw038
[Indexed for MEDLINE]
Free PMC Article

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