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A novel DNA sequence similarity calculation based on simplified pulse-coupled neural network and Huffman coding

Author

Listed:
  • Jin, Xin
  • Nie, Rencan
  • Zhou, Dongming
  • Yao, Shaowen
  • Chen, Yanyan
  • Yu, Jiefu
  • Wang, Quan

Abstract

A novel method for the calculation of DNA sequence similarity is proposed based on simplified pulse-coupled neural network (S-PCNN) and Huffman coding. In this study, we propose a coding method based on Huffman coding, where the triplet code was used as a code bit to transform DNA sequence into numerical sequence. The proposed method uses the firing characters of S-PCNN neurons in DNA sequence to extract features. Besides, the proposed method can deal with different lengths of DNA sequences. First, according to the characteristics of S-PCNN and the DNA primary sequence, the latter is encoded using Huffman coding method, and then using the former, the oscillation time sequence (OTS) of the encoded DNA sequence is extracted. Simultaneously, relevant features are obtained, and finally the similarities or dissimilarities of the DNA sequences are determined by Euclidean distance. In order to verify the accuracy of this method, different data sets were used for testing. The experimental results show that the proposed method is effective.

Suggested Citation

  • Jin, Xin & Nie, Rencan & Zhou, Dongming & Yao, Shaowen & Chen, Yanyan & Yu, Jiefu & Wang, Quan, 2016. "A novel DNA sequence similarity calculation based on simplified pulse-coupled neural network and Huffman coding," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 325-338.
    • Handle: RePEc:eee:phsmap:v:461:y:2016:i:c:p:325-338
    DOI: 10.1016/j.physa.2016.05.004
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    References listed on IDEAS

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    1. Buldyrev, S.V. & Dokholyan, N.V. & Goldberger, A.L. & Havlin, S. & Peng, C.-K. & Stanley, H.E. & Viswanathan, G.M., 1998. "Analysis of DNA sequences using methods of statistical physics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 249(1), pages 430-438.
    2. He, Ping-an & Wei, Jinzhou & Yao, Yuhua & Tie, Zhixin, 2012. "A novel graphical representation of proteins and its application," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 93-99.
    3. Hou, Wenbing & Pan, Qiuhui & He, Mingfeng, 2014. "A novel representation of DNA sequence based on CMI coding," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 409(C), pages 87-96.
    4. Peng, C.-K. & Buldyrev, S.V. & Goldberger, A.L. & Havlin, S. & Sciortino, F. & Simons, M. & Stanley, H.E., 1992. "Fractal landscape analysis of DNA walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 191(1), pages 25-29.
    5. Liao, Bo & Xiang, Qilin & Cai, Lijun & Cao, Zhi, 2013. "A new graphical coding of DNA sequence and its similarity calculation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4663-4667.
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    Cited by:

    1. Abdur Rasool & Qiang Qu & Yang Wang & Qingshan Jiang, 2022. "Bio-Constrained Codes with Neural Network for Density-Based DNA Data Storage," Mathematics, MDPI, vol. 10(5), pages 1-21, March.

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