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

Journal of Applied Genetics volume 61, pages 231–238 (2020)Cite this article

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

  1. Nearest distance (single linkage method)

  2. Furthest distance (complete linkage method)

  3. Unweighted pair group method average (UPGMA, group average)

  4. Weighted pair group method average (WPGMA)

  5. Unweighted pair group method centroid (UPGMC)

  6. 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.

Author information

Affiliations

  1. Department of Animal Science Research, Guilan Agricultural and Natural Resources Research and Education Center, AREEO, Rasht, Iran

    Houshang Dehghanzadeh

  2. Department of Animal Science, Faculty of Agriculture, University of Yasouj, P. O. Box: 75914, Yasouj, Iran

    Mostafa Ghaderi-Zefrehei

  3. Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

    Seyed Ziaeddin Mirhoseini

  4. Animal Science Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

    Saeid Esmaeilkhaniyan

  5. Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln, New Zealand

    Ishaku Lemu Haruna & Hamed Amirpour Najafabadi

Authors
  1. Houshang Dehghanzadeh
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  2. Mostafa Ghaderi-Zefrehei
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  3. Seyed Ziaeddin Mirhoseini
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  6. Hamed Amirpour Najafabadi
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Contributions

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.

Corresponding author

Correspondence to Mostafa Ghaderi-Zefrehei.

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This article does not contain any studies with animals performed by any of the authors.

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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

Keywords

  • Information theory
  • Dairy cattle
  • Kullback-Leibler divergence
  • Gene clustering