Abstract
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.
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Notes
Nearest distance (single linkage method)
Furthest distance (complete linkage method)
Unweighted pair group method average (UPGMA, group average)
Weighted pair group method average (WPGMA)
Unweighted pair group method centroid (UPGMC)
Weighted pair group method centroid (WPGMC)
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Acknowledgments
The authors express their best to Kamyar Shiouee for his comments on MATLAB codes, Dr. Saeid Ansari-Mahyari for providing necessary infrastructure and resources, and Professor Ahmad Oryan for his supports.
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Houshang Dehghanzadeh run algorithm, wrote up the paper, and did the literature review.
Mostafa Ghaderi-Zefrehei was the principle investigator of this study, developed ideas from the scratch, conduced computational pipelines, and participated in writing up paper.
Seyed Ziaeddin Mirhoseini and Saeid Esmaeilkhaniyan performed data collection and explored the results.
Ishaku Lemu Haruna performed editing the paper.
Hamed Amirpour-Najafabadi has been involved in drafting the manuscript and revising it critically for important intellectual content and final approval of the version to be published.
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Communicated by: Maciej Szydlowski
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Dehghanzadeh, H., Ghaderi-Zefrehei, M., Mirhoseini, S.Z. et al. A new DNA sequence entropy-based Kullback-Leibler algorithm for gene clustering. J Appl Genetics 61, 231–238 (2020). https://doi.org/10.1007/s13353-020-00543-x
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DOI: https://doi.org/10.1007/s13353-020-00543-x