ReLume: Enhancing DNA storage data reconstruction with flow network and graph partitioning.

Area of Science:

• Biotechnology
• Computer Science
• Data Storage

Background:

• DNA data storage offers a promising alternative to traditional silicon-based storage.
• Current DNA storage methods face challenges with data errors and long-term durability.
• Efficient data reconstruction is crucial for practical DNA data storage applications.

Purpose of the Study:

• To introduce ReLume, a novel DNA data reconstruction method.
• To address accuracy and hardware limitations in DNA data storage.
• To improve the durability and reliability of stored DNA data.

Main Methods:

• Developed ReLume, a data reconstruction method utilizing flow networks and graph partitioning technology.
• Designed ReLume to handle millions of reads on standard hardware (24 GB RAM laptop).
• Evaluated ReLume's performance against existing DNA data storage reconstruction methods.

Main Results:

• ReLume significantly improves sequence recovery rates, more than doubling them in some cases.
• The method reduces memory usage by approximately 60% compared to other approaches.
• ReLume demonstrates 10 times greater durability, ensuring data integrity for up to 100 years.
• Achieved high sequence recovery rates (73% and 93.2%) on wet lab DNA storage datasets.

Conclusions:

• ReLume effectively overcomes accuracy and hardware limitations in DNA data storage.
• The method provides a feasible solution for portable and reliable DNA data storage.
• ReLume enhances the overall viability of DNA as a long-term data archiving solution.