Design of DNA Storage Coding with Enhanced Constraints.

Area of Science:

• Bioinformatics
• Data Storage Technologies
• Molecular Engineering

Background:

• Traditional data storage methods face limitations with increasing global data demands.
• DNA storage offers a high-density alternative but suffers from sequencing errors.
• Existing DNA coding methods require enhanced robustness against errors.

Purpose of the Study:

• To propose a novel method for enhancing the robustness of DNA storage coding sets.
• To reduce error rates in DNA data storage systems.
• To improve the quality and reliability of DNA-based data encoding.

Main Methods:

• Introduced a repeat tandem sequence constraint to minimize secondary structures in DNA coding.
• Developed an improved Dynamic Time Warping (DTW) distance constraint for accurate non-specific hybridization evaluation.
• Proposed a new optimization algorithm, ROEAO (Random Opposition-based Learning with Eddy Jump strategy and Aquila Optimizer).
• Utilized ROEAO to construct DNA coding sets with both traditional and enhanced constraints.

Main Results:

• The ROEAO algorithm successfully constructed DNA coding sets.
• Coding sets built with enhanced constraints demonstrated improved quality metrics.
• Enhanced constraint coding sets achieved a larger lower bound compared to traditional ones.
• Evaluations included assessing secondary structure formation and melting temperature stability.

Conclusions:

• The proposed enhanced constraints and ROEAO algorithm significantly improve DNA storage coding quality.
• This approach effectively reduces error rates, making DNA storage more reliable.
• The findings pave the way for more robust and efficient DNA data storage solutions.