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Randomized DNA Base Sequence Design by Using Degenerate Bases for DNA Data Storage.

Seongjun Seo1, Anshula Tandon1, Thi Bich Ngoc Nguyen1

  • 1Department of Physics, Institute of Basic Science, and Sungkyunkwan Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea.

ACS Applied Bio Materials
|October 28, 2025
PubMed
Summary
This summary is machine-generated.

We developed RN-B#, a DNA data storage framework using degenerate bases to boost information density and reduce errors. This method enables robust, high-capacity storage and accurate data recovery, advancing DNA data storage technology.

Keywords:
DNAdata storagedegenerate baserandomizationsequence design

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Area of Science:

  • Biotechnology
  • Bioinformatics
  • Synthetic Biology

Background:

  • DNA data storage offers high density but requires efficient encoding.
  • Current methods face challenges in balancing compactness, stability, and fidelity.
  • Degenerate bases present an opportunity to enhance DNA storage capabilities.

Purpose of the Study:

  • To introduce a novel randomized DNA base sequence design framework, RN-B#.
  • To enhance information density and minimize redundancy in DNA data storage.
  • To demonstrate the tunability and effectiveness of RN-B# for robust data storage.

Main Methods:

  • Implemented rule-based encoding systems (R∞-B32, R2-B52, R0-B16) with constraints on homopolymer length and degenerate base positioning.
  • Encoded black-white binary image data using the RN-B# framework.
  • Validated data recovery via Sanger sequencing and developed probabilistic models for sequencing accuracy.

Main Results:

  • Achieved a maximum theoretical information density of 3.91 bits/nt.
  • Demonstrated successful image recovery with an average sequence identity of up to 75%.
  • Quantified sequencing accuracy based on sequencing depth and degenerate base complexity.

Conclusions:

  • The RN-B# framework provides a versatile platform for high-capacity DNA data storage.
  • Degenerate bases significantly enhance information density and sequence stability.
  • The developed models accurately predict sequencing accuracy, crucial for reliable data retrieval.