A new DNA sequence entropy-based Kullback-Leibler algorithm for gene clustering

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 this study, for each gene and its exons sets, the entropy was calculated in orders one to four. 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. To aggregate the results of clustering, the AdaBoost algorithm was used. Finally, the results of the 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). Following our findings on investigating the results of genes metabolic pathways based on the gene annotations, it was revealed that our proposed clustering method yielded correct, logical, and fast results. This method at the same that had not had the disadvantages of aligning allowed the genes with actual length and content to be considered and also did not require high memory for large-length sequences. We believe 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

Molecular tagging of agronomic traits using simple sequence repeats: Informative markers for almond (Prunus dulcis) molecular breeding

Informative markers are the most applicable genetic information in breeding schemes. Association studies which soundly integrate molecular and morphological data are the best choice to find informative markers, particularly in crops that are limited to only one generation per year. Therefore, in the present research the associations between different morphological traits and highly polymorphic SSRs were studied to find possible informative markers for some morphological and/or agronomical traits in almond. In total, 39 morphological traits were recorded during two years among 53 almond genotypes/cultivars. Extracted almond genomic DNA was PCR-amplified using 9 pairs flanking SSRs sequences previously cloned and sequenced specifically for almond. For finding association between molecular markers and morphological traits and identification of possible informative markers, Pearson correlation and stepwise regression analysis were employed. The results revealed a significant correlation between the morphological traits and the studied microsatellite loci. A total of 141 positive markers out of 556 polymorphic bands were identified for different traits. For some of the morphological traits more than one informative marker was detected, which consequently finding their additive effects, degree of dominance and sum of the positive and negative effects need further analysis. These informative markers can be considered as postulated candidate markers for scanning the genome for related morphological (particularly agronomical) traits, mapping and finally marker assisted selection programs

Simple hierarchical and general nonlinear growth modeling in sheep

Differential equations and advanced statistical models have been used to predict growth phenomena. In the present study, general nonlinear growth functions such as von Bertalanffy, Gompertz, logistic, and Brody, along with hierarchical modeling were applied to investigate the phenotypic growth pattern of Iranian Lori-Bakhtiari sheep. Growth data from 1410 Lori- Bakhtiari lambs were used in the present study. The results showed that the Brody function outperformed the other three nonlinear growth functions. In addition, including hierarchical growth modeling results allowed the adoption of many random effect structures, suggesting that hierarchical growth modeling has a useful role in growth data modeling. This method provides an estimation of growth parameters based on individual animals, improving individual growth selection. The results suggest this approach for growth modeling. Combining the strength of individual growth modeling with general growth modeling, e.g., von Bertalanffy, Gompertz, logistic, and Brody would be deeply appealing in the future. In this regard, dealing with sheep growth phenomenon using pure mathematical models, i.e. grey system theory models that could be new powerful prediction tools for breeders and experts, has not been done yet. However, running the analysis on large datasets will require significantly higher computational power than is ordinarily available