RETRACTED ARTICLE: A new DNA sequence entropy-based Kullback-Leibler algorithm for gene clustering

• Published in: Journal of applied genetics Vol. 61; no. 2; pp. 231 - 238
• Main Authors: Dehghanzadeh, Houshang, Ghaderi-Zefrehei, Mostafa, Mirhoseini, Seyed Ziaeddin, Esmaeilkhaniyan, Saeid, Haruna, Ishaku Lemu, Amirpour Najafabadi, Hamed
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2020; Springer; Springer Nature B.V
• Subjects: Algorithms; Analysis; Animal Genetics and Genomics; Animal Genetics • Original Paper; Annotations; Biomedical and Life Sciences; Biomedical engineering; Centroids; Clustering; Deoxyribonucleic acid; Divergence; DNA; DNA sequencing; Entropy; Entropy (Information theory); Exons; Genes; Human Genetics; Information theory; Life Sciences; Machine learning; Mathematical analysis; Metabolic pathways; Microbial Genetics and Genomics; Nucleotide sequence; Nucleotide sequencing; Plant Genetics and Genomics; Sequences; Software engineering; Iran; Kullback-Leibler divergence; Gene clustering; Dairy cattle; Information theory
• ISSN: 1234-1983; 2190-3883
• DOI: 10.1007/s13353-020-00543-x

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.