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High-density information storage and random access scheme using synthetic DNA.

Shufang Zhang1, Jianjun Wu1, Beibei Huang1

  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China.

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DNA data storage offers high density and longevity but faces cost and access challenges. This study introduces a novel DNA coding scheme using convolutional neural networks to improve storage density and random access efficiency.

Keywords:
DNA information storageDNA moleculesHigh densitySynthetic DNA

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Area of Science:

  • Biotechnology
  • Data Storage
  • Bioinformatics

Background:

  • DNA offers exceptional storage density, long-term stability, and low energy consumption, positioning it as a promising next-generation storage medium.
  • Current DNA data storage methods grapple with high synthesis costs and inefficient random access, limiting practical applications.
  • Developing advanced DNA coding schemes is crucial for overcoming these limitations and realizing the full potential of DNA storage technology.

Purpose of the Study:

  • To propose a novel, high-density DNA coding scheme to reduce synthesis costs and enhance random access efficiency.
  • To address biological constraints, specifically homopolymer length and GC content, within the DNA mapping process.
  • To improve the overall information storage density of DNA-based data storage systems.

Main Methods:

  • A codebook-based DNA mapping method was developed, adhering to biological constraints like homopolymer length and GC content.
  • A content-encoded random access method was designed for efficient and selective retrieval of specific files from DNA pools.
  • Convolutional neural networks were integrated with mapping techniques to generate DNA base sequences, optimizing storage density.

Main Results:

  • The proposed DNA mapping method successfully satisfied biological constraints, ensuring sequence stability and synthesizability.
  • The content-encoded random access method demonstrated efficient and selective data retrieval capabilities.
  • Experimental comparisons showed that the developed scheme achieved superior information storage density compared to existing DNA storage methods.

Conclusions:

  • The proposed DNA coding scheme effectively enhances storage density and random access efficiency, addressing key limitations in DNA data storage.
  • The integration of convolutional neural networks and codebook-based mapping offers a promising approach for future DNA storage technologies.
  • This research contributes to making DNA storage a more cost-effective and practical solution for large-scale data archiving.