Gene tree construction using Kullback-Leibler divergence on milk governing genes in dairy cattle

Information theory is a branch of mathematics that overlaps with communications, biology. The aim of the current study was to provide a method for clustering a number of Milk Governing Genes in Dairy Cattle using an algorithm based on Kullback-Leibler divergence. In this study, after retrieving gene and exon DNA sequences affecting milk yield in dairy cattle, the entropy in orders one to four was calculated. In order to extract gene distances, Kullback-Leibler divergence over three different methods was calculated. The first and second methods were based on the genes alignment but the third method was based on non-alignment and the relative entropy of the genes. The results of each method of Kullback-Leibler divergence over DNA and exon sequences were entered as input into 7 general clustering algorithms: Single, Complete, Average, Weighted, Centroid, Median and K-Means. Integrated result of each clustering algorithm due to AdaBoost algorithm, which implied as gene tree, indicated that the third method was based on the relative entropy of the genes, biologically grouped set of genes as it was proved by their gene annotation using GeneMANIA. We believe that the proposed method might be used with other DNA based clustering competitive methods and therefore, it can be used to group set of genes in other species.