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CLGBO: An Algorithm for Constructing Highly Robust Coding Sets for DNA Storage.

Yanfen Zheng1, Jieqiong Wu1, Bin Wang1

  • 1The Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, School of Software Engineering, Dalian University, Dalian, China.

Frontiers in Genetics
|May 21, 2021
PubMed
Summary
This summary is machine-generated.

New DNA storage methods use an enhanced optimizer and non-adjacent subsequences to create larger, more robust coding sets, improving data storage capacity and reducing errors in the big data era.

Keywords:
CLGBODNA coding setsDNA storagenon-adjacent subsequence constraintprimer and address sequences

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Area of Science:

  • Bioinformatics and Computational Biology
  • Data Storage Technologies
  • Information Science

Background:

  • Exponential growth of global data necessitates novel storage solutions beyond current capacities.
  • Deoxyribonucleic acid (DNA) storage offers high density, large capacity, and durability, making it a promising medium.
  • Primer and address sequences are critical components in DNA data storage systems.

Purpose of the Study:

  • To develop an enhanced gradient-based optimizer with Cauchy and Levy mutation strategy (CLGBO) for constructing DNA coding sets.
  • To improve the robustness and error rates of DNA storage coding sets.
  • To address limitations in current DNA storage, such as errors caused by consecutive repetitive subsequences.

Main Methods:

  • Implementation of the Cauchy and Levy gradient-based optimizer (CLGBO) for generating DNA primer and address libraries.
  • Introduction of a non-adjacent subsequence constraint to mitigate storage errors.
  • Experimental evaluation of the CLGBO algorithm's performance in constructing DNA coding sets.

Main Results:

  • The CLGBO algorithm increased the lower bounds of DNA storage coding sets by 4.3-13.5% compared to previous methods.
  • The non-adjacent subsequence constraint effectively reduced error rates during the DNA storage process.
  • Larger and more robust DNA coding sets were successfully constructed using the CLGBO algorithm and the new constraint.

Conclusions:

  • The CLGBO algorithm represents a significant advancement in optimizing DNA coding sets for data storage.
  • The non-adjacent subsequence constraint is crucial for enhancing the reliability of DNA data storage.
  • This research contributes to overcoming challenges in big data storage by advancing DNA-based solutions.