Modeling DNA storage retrieval reliability via sequencing coverage depth

Area of Science:

• Biotechnology
• Data Storage
• Bioinformatics

Background:

• DNA data storage offers high density and durability but faces challenges in sequencing cost and throughput.
• Accurate modeling of sequencing coverage depth is essential for reliable data retrieval, but current models often assume uniform channels.

Purpose of the Study:

• To develop a quantitative framework for analyzing nonuniform coverage depth in DNA storage channels.
• To improve data retrieval reliability and sequencing efficiency in DNA data storage systems.

Main Methods:

• Developed a nonuniform coverage-depth analysis framework modeling polymerase chain reaction and sequencing data.
• Utilized a log-normal distribution to represent empirical channel distributions.
• Derived theoretical models (CCP and CQT) for noisy channels and analyzed the Minimumicosia (MDS) coverage depth problem.
• Conducted extensive Monte Carlo simulations to compare theoretical models against uniform channel assumptions.

Main Results:

• Derived the minimum sequencing coverage depth required for complete decoding in noiseless nonuniform channels.
• Presented two theories (CCP and CQT) for noisy channels, with CQT offering refined estimates.
• Demonstrated through simulations that the proposed framework outperforms uniform channel assumptions across various parameters.
• Developed an interactive web platform for parameter tuning and visualization.

Conclusions:

• The developed framework provides a theoretical foundation for understanding and optimizing DNA storage systems.
• The framework and associated tools enhance data retrieval reliability and sequencing efficiency, paving the way for practical DNA data storage.
• Accurate modeling of channel nonuniformity is critical for advancing DNA storage technology.