DNA Bloom Filter enables anti-contamination and file version control for DNA-based data storage.

Area of Science:

• Bioinformatics
• Data Storage Technologies
• Molecular Engineering

Background:

• DNA data storage offers high density and longevity but faces challenges with errors during synthesis, storage, and sequencing.
• Existing error correction methods for DNA storage can reduce data density or increase computational complexity.

Purpose of the Study:

• To develop a novel, efficient method for error and contamination filtering in DNA data storage.
• To implement a cost-effective file version control system for DNA-based data storage.

Main Methods:

• Application of Bloom Filters, a space-efficient probabilistic data structure, for filtering non-target DNA sequences.
• Utilizing Counting Bloom Filters for file version control in DNA storage systems.
• Development of a modified system based on yin-yang codec for cost-efficient version control.

Main Results:

• The Bloom Filter method effectively filters out incorrect DNA sequences (non-target sequences) during sequencing analysis.
• Experimental results confirm the universal and efficient filtering capabilities of the proposed approach.
• The Counting Bloom Filter successfully implements file version control, significantly reducing synthesis costs for modified DNA files.

Conclusions:

• Bloom Filters provide an efficient and space-saving solution for anti-error and anti-contamination in DNA data storage.
• The integration of Bloom Filters enhances the reliability and practicality of DNA storage systems.
• The developed system offers a cost-effective approach to managing versions of DNA-form files, crucial for iterative data updates.