Resolving the Gene Tree and Species Tree Problem by Phylogenetic Mining

The gene tree and species tree problem remains a central problem in phylogenomics. To overcome this problem, gene concatenation approaches have been used to combine a certain number of genes randomly from a set of widely distributed orthologous genes selected from genome data to conduct phylogenetic analysis. The random concatenation mechanism prevents us from the further investigations of the inner structures of the gene data set employed to infer the phylogenetic trees and locates the most phylogenetically informative genes. In this work, a phylogenomic mining approach is described to gain knowledge from a gene data set by clustering genes in the gene set through a self-organizing map (SOM) to explore the gene dataset inner structures. From this, the most phylogenetically informative gene set is created by picking the maximum entropy gene from each cluster to infer phylogenetic trees by phylogenetic analysis. Using the same data set, the phylogenetic mining approach performs better than the random gene concatenation approach.