Taxon ordering in phylogenetic trees: a workbench test
1 Department of Animal Production, Epidemiology and Ecology, Faculty of Veterinary Medicine, University of Torino, via Leonardo da Vinci 44, 10095, Grugliasco (TO), Italy
2 Molecular Biotechnology Center, University of Torino, via Nizza 52, 10126, Torino, Italy
3 Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, 1656 Linden Drive, Madison, Wisconsin, 53706, USA
BMC Bioinformatics 2011, 12:58 doi:10.1186/1471-2105-12-58Published: 22 February 2011
Phylogenetic trees are an important tool for representing evolutionary relationships among organisms. In a phylogram or chronogram, the ordering of taxa is not considered meaningful, since complete topological information is given by the branching order and length of the branches, which are represented in the root-to-node direction. We apply a novel method based on a (λ + μ)-Evolutionary Algorithm to give meaning to the order of taxa in a phylogeny. This method applies random swaps between two taxa connected to the same node, without changing the topology of the tree. The evaluation of a new tree is based on different distance matrices, representing non-phylogenetic information such as other types of genetic distance, geographic distance, or combinations of these. To test our method we use published trees of Vesicular stomatitis virus, West Nile virus and Rice yellow mottle virus.
Best results were obtained when taxa were reordered using geographic information. Information supporting phylogeographic analysis was recovered in the optimized tree, as evidenced by clustering of geographically close samples. Improving the trees using a separate genetic distance matrix altered the ordering of taxa, but not topology, moving the longest branches to the extremities, as would be expected since they are the most divergent lineages. Improved representations of genetic and geographic relationships between samples were also obtained when merged matrices (genetic and geographic information in one matrix) were used.
Our innovative method makes phylogenetic trees easier to interpret, adding meaning to the taxon order and helping to prevent misinterpretations.