2015/10/31 by Kevin Gori, Tomasz Suchan, Nadir Álvarez +3 · 77 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · #Artificial intelligence #Biology #Cluster analysis #Coalescent theory #Computational biology #Computer science #Context (archaeology) #Evolutionary biology #Gene #Genetic diversity and population structure #Genetics #Genomics and Phylogenetic Studies #Inference #Locus (genetics) #Mathematics #Phylogenetic tree #Tree (set theory) #Yeasts and Rust Fungi Studies #q-bio.GN #q-bio.PE
paper · pdf · doi:10.1093/molbev/msw038
published in Molecular Biology and Evolution 33(6), 1590-1605 (Oxford University Press) · accepted at Molecular Biology and Evolution
openalex publication_date 2016/02/17 · arxiv created 2016/03/09 · arxiv updated 2016/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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).