A digital twin for DNA data storage based on comprehensive quantification of errors and biases

Area of Science:

• Biotechnology
• Bioinformatics
• Data Storage

Background:

• Synthetic DNA offers high-density, long-term data archiving.
• Errors and biases in DNA data storage necessitate error correction coding (ECC), increasing redundancy.
• Lack of error data and modeling tools hinders ECC development.

Purpose of the Study:

• To comprehensively characterize error sources and biases in DNA data storage workflows.
• To develop a digital twin for simulating DNA data storage processes.
• To enable data-driven ECC development and optimize redundancy strategies.

Main Methods:

• Characterization of errors from DNA synthesis, PCR, aging, and sequencing.
• Development of a digital twin using data from 40 sequencing experiments.
• Simulation of common DNA data storage workflows and experimental reproduction.

Main Results:

• Identified and quantified errors and biases across key DNA data storage steps.
• Validated the digital twin's ability to accurately simulate experimental workflows.
• Demonstrated the digital twin's utility in replacing experiments and informing ECC design.

Conclusions:

• A digital twin can accurately model DNA data storage workflows, reducing the need for physical experiments.
• This approach facilitates data-driven development of more efficient error correction coding (ECC).
• Optimized ECC design leads to tangible cost savings in DNA data storage systems.