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Multi-file dynamic compression method based on classification algorithm in DNA storage.

Kun Bi1, Qi Xu2, Xin Lai2,3

  • 1State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, 210096, Nanjing, China. bik@seu.edu.cn.

Medical & Biological Engineering & Computing
|June 26, 2024
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Summary
This summary is machine-generated.

This study introduces a machine learning approach for DNA data storage, dynamically selecting compression methods per file. This dynamic compression significantly boosts efficiency and reduces costs for DNA data storage solutions.

Keywords:
Classification algorithmCompression rateDNA storageData compression

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Area of Science:

  • Biotechnology
  • Computer Science
  • Data Storage

Background:

  • Exponential data growth necessitates advanced storage solutions like DNA storage.
  • High synthesis and sequencing costs currently limit DNA storage adoption.
  • Data pre-compression is key to reducing DNA storage expenses.

Purpose of the Study:

  • To develop a multi-file dynamic compression method for DNA storage.
  • To minimize data volume for DNA storage using intelligent compression selection.
  • To reduce the overall cost of DNA data storage.

Main Methods:

  • Applied four distinct compression methods to a dataset.
  • Utilized file type and size as features for machine learning classification.
  • Trained seven machine learning algorithms to select optimal compression per file.
  • Identified k-nearest neighbor as the top-performing algorithm.

Main Results:

  • The k-nearest neighbor model achieved over 85% accuracy in selecting compression methods.
  • A compression rate of 30.85% was achieved, exceeding traditional methods by over 4.5%.
  • Potential cost savings for DNA storage range from $0.48 to $3 billion per terabyte.

Conclusions:

  • The proposed multi-file dynamic compression method significantly enhances compression efficiency compared to single-method approaches.
  • This method substantially decreases DNA storage costs.
  • The findings facilitate the broader implementation of DNA storage technology.