Abstract
A major goal of many evolutionary analyses is to determine the true evolutionary history of an organism. Molecular methods that rely on the phylogenetic signal generated by a few to a handful of loci can be used to approximate the evolution of the entire organism but fall short of providing a global, genome-wide, perspective on evolutionary processes. Indeed, individual genes in a genome may have different evolutionary histories. Therefore, it is informative to analyze the number and kind of phylogenetic topologies found within an orthologous set of genes across a genome. Here we present PhyBin: a flexible program for clustering gene trees based on topological structure. PhyBin can generate bins of topologies corresponding to exactly identical trees or can utilize Robinson-Fould’s distance matrices to generate clusters of similar trees, using a user-defined threshold. Additionally, PhyBin allows the user to adjust for potential noise in the dataset (as may be produced when comparing very closely related organisms) by pre-processing trees to collapse very short branches or those nodes not meeting a defined bootstrap threshold. As a test case, we generated individual trees based on an orthologous gene set from 10 Wolbachia species across four different supergroups (A–D) and utilized PhyBin to categorize the complete set of topologies produced from this dataset. Using this approach, we were able to show that although a single topology generally dominated the analysis, confirming the separation of the supergroups, many genes supported alternative evolutionary histories. Because PhyBin’s output provides the user with lists of gene trees in each topological cluster, it can be used to explore potential reasons for discrepancies between phylogenies including homoplasies, long-branch attraction, or horizontal gene transfer events.
Highlights
The advent of genomic sequencing has produced a large amount of data available for phylogenetic analysis and many researchers have attempted to utilize the phylogenetic signal found across the bacterial genome to develop species trees (Daubin, Gouy & Perriere, 2001; Sicheritz-Ponten & Andersson, 2001; Daubin, Moran & Ochman, 2003; Bapteste et al, 2004; Zhaxybayeva et al, 2006; Ellegaard et al, 2013)
What has become clear from these analyses is that significant fractions of bacterial genomes do not follow the evolutionary history of their resident genome (Bapteste et al, 2004)
We used PhyBin to identify how many phylogenies within the Wolbachia orthologous gene set support the supergroup divisions proposed by multi-locus sequence typing (Baldo & Werren, 2007)
Summary
The advent of genomic sequencing has produced a large amount of data available for phylogenetic analysis and many researchers have attempted to utilize the phylogenetic signal found across the bacterial genome to develop species trees (Daubin, Gouy & Perriere, 2001; Sicheritz-Ponten & Andersson, 2001; Daubin, Moran & Ochman, 2003; Bapteste et al, 2004; Zhaxybayeva et al, 2006; Ellegaard et al, 2013). After the user applies their chosen ortholog prediction and tree-building algorithms, PhyBin offers a quick way to visualize and browse the different evolutionary histories, either binned by topology and sorted by bin size, or in the form of a full hierarchical clustering based on Robinson-Foulds distance: i.e., a tree of trees. The Newick format trees that resulted were used as input to PhyBin. The number of orthologous genes identified in this manner across all 10 taxa was 503.
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