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Updated: Jan 10, 2026
16:24
Analyzing and Building Nucleic Acid Structures with 3DNA
Published on: April 26, 2013
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Quantitative Assessment of Randomized DNA Base Sequences Using Multi-Model Physical Analysis for High-Fidelity Data
Seongjun Seo1, Thi Hong Nhung Vu1, Anshula Tandon1
1Department of Physics, Institute of Basic Science, and Sungkyunkwan Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, Republic of Korea.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|November 21, 2025
Summary
This study introduces a framework to optimize DNA data storage sequences, reducing errors from homopolymers and bias. The new design rules enhance stability and reliability for ultra-dense digital information storage in DNA.
Area of Science:
- Biotechnology
- Data Storage
- Bioinformatics
Background:
- DNA offers ultra-dense, long-term digital data storage potential.
- Current DNA sequence design is limited by homopolymer formation and compositional bias, affecting accuracy.
Purpose of the Study:
- To develop a quantitative framework for optimizing DNA base sequence design rules.
- To improve synthesis, sequencing, and decoding accuracy for DNA data storage.
Main Methods:
- Utilized three physics-inspired models: active particle trajectories, inverse Ising model, and a logic algorithm system.
- Applied encoding schemes with varying homopolymer constraints to binary image data.
- Systematically evaluated and optimized randomized DNA base sequence design rules.
Main Results:
- Stringent randomization rules significantly reduced homopolymer length and balanced GC content.
- Enhanced sequence randomness was observed.
- Experimental validation confirmed high decoding fidelity (95-98%) via PCR and Sanger sequencing.
Conclusions:
- The developed multi-model assessment provides a robust strategy for designing stable and reliable DNA sequences.
- This approach enhances the scalability of DNA for future molecular data storage systems.
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