Clustering dairy cattle genes by Kullback-Leibler divergence

摘要

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. A Calgorithm base 2 to calculate Shannon entropy of gene DNA sequences was used to extract cluster genes governing milk production in dairy cows by Kullback-Leibler (KL) divergence. KL was based on nucleotide similarity (KLA), difference (KLB) and different order of Relative Entropy (KLH). AdaBoost algorithm was used to interpret clustering results. Examples of results: STX3(nnucleotide =79347) and CD14 (nnucleotide = 1417) were longest and shortest genes, respectively. 258 exons were identified whereinexon 1 of HSPA1A(nnucleotide =2101) and HSPA5(nnucleotide = 20) were longest and shortest. LCP1 and ABCG2 genes had highest number of exons (nexon=16) and HSPA1A and YWHAG(nexon = 1) had shortest number exons for this set of genes. Findings suggested that exons with maximum entropy value are likely to be suitable for genotype analysis using molecular markers and that both coding and non-coding sequences had low or high complexity. KL divergence can be used to cluster large sets of dairy cattle genes with other methods to group biologically relevant sets of genes.