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Bio-Constrained Codes with Neural Network for Density-Based DNA Data Storage

Author

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  • Abdur Rasool

    (Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China)

  • Qiang Qu

    (Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Yang Wang

    (Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Qingshan Jiang

    (Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

Abstract

DNA has evolved as a cutting-edge medium for digital information storage due to its extremely high density and durable preservation to accommodate the data explosion. However, the strings of DNA are prone to errors during the hybridization process. In addition, DNA synthesis and sequences come with a cost that depends on the number of nucleotides present. An efficient model to store a large amount of data in a small number of nucleotides is essential, and it must control the hybridization errors among the base pairs. In this paper, a novel computational model is presented to design large DNA libraries of oligonucleotides. It is established by integrating a neural network (NN) with combinatorial biological constraints, including constant GC-content and satisfying Hamming distance and reverse-complement constraints. We develop a simple and efficient implementation of NNs to produce the optimal DNA codes, which opens the door to applying neural networks for DNA-based data storage. Further, the combinatorial bio-constraints are introduced to improve the lower bounds and to avoid the occurrence of errors in the DNA codes. Our goal is to compute large DNA codes in shorter sequences, which should avoid non-specific hybridization errors by satisfying the bio-constrained coding. The proposed model yields a significant improvement in the DNA library by explicitly constructing larger codes than the prior published codes.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:845-:d:765759
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    References listed on IDEAS

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    1. Qian Liu & Li Fang & Guoliang Yu & Depeng Wang & Chuan-Le Xiao & Kai Wang, 2019. "Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    2. Nick Goldman & Paul Bertone & Siyuan Chen & Christophe Dessimoz & Emily M. LeProust & Botond Sipos & Ewan Birney, 2013. "Towards practical, high-capacity, low-maintenance information storage in synthesized DNA," Nature, Nature, vol. 494(7435), pages 77-80, February.
    3. 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.
    4. Jinny X. Zhang & Boyan Yordanov & Alexander Gaunt & Michael X. Wang & Peng Dai & Yuan-Jyue Chen & Kerou Zhang & John Z. Fang & Neil Dalchau & Jiaming Li & Andrew Phillips & David Yu Zhang, 2021. "A deep learning model for predicting next-generation sequencing depth from DNA sequence," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
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