Possibility of application of relative entropy in clustering of some milk governing genes in dairy cattle

Apart from the fact that milk plays an important role in human nutrition, increasing milk production or changing its composition has attracted the attention of animal breeders, therefore, it is crucial to study and evaluate the genes underpinning milk production and its composition. Information theory is a branch of mathematics that overlaps with communications, biology, and medical engineering. Entropy is a measure of uncertainty in the set of information. In his famous article in 1948, Shannon introduced this concept and used its results in a number of basic issues of coding and data transferring theory, which forms the basis of new information theory. Information theory is used in genetic and bioinformatics analyses and can be used for many analyses related to the structures and sequences. Bio-computational grouping of genes facilitates genetic analysis, sequencing and structural-based analyses
DNA sequence of 30 genes involved with milk protein production were extracted ad hoc from NCBI genome database and stored in FASTA format. In this study, for each gene and its exons sets, the entropy was calculated in orders one to four. In this way, the Markov chain up to order 3 was used. Based on the relative entropy of genes and exons, kullback-Leibler divergence was calculated. After obtaining the kullback-Leibler distance for genes and exons sets, the results were entered as input into 7 clustering algorithms: Single, Complete, Average, Weighted, Centroid, Median and K-Means. In order to aggregate the results of clustering, AdaBoost algorithm was used. Finally, the results of AdaBoost algorithm were investigated by GeneMANIA prediction server to explore the results from gene annotation point of view. All calculations were performed using the MATLAB Engineering Software (2015)
By investigating the results of genes metabolic pathways based on their gene annotations, it was turned out that proposed clustering method, yielded correct, logical and fast results. This method at the same that that hadnt had the disadvantages of aligning allowed the genes with actual length and content to be considered and also didnt require high memory for large-length sequences.
It can be concluded that the performance of the proposed method could be used with other competitive gene clustering methods to group biologically relevant set of genes Also, the proposed method can be seen as a predictive method for those genes bearing up weak genomic annotations.