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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

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Summary
This summary is machine-generated.

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.

Keywords:
3‐input 1‐output logic algorithmDNA data storageactive particle trajectory modelinverse Ising modelsequence randomness

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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.