Deep DNA Storage: Scalable and Robust DNA Storage via Coding Theory and Deep Learning
This work addresses bottlenecks in DNA storage retrieval for commercial applications, offering incremental improvements over existing methods.
The paper tackled the scalability and accuracy trade-off in DNA-based storage by developing a pipeline combining deep neural networks, error-correcting codes, and a safety margin, resulting in up to a 3200x speed increase, 40% accuracy improvement, and a code rate of 1.6 bits per base in high noise.
DNA-based storage is an emerging technology that enables digital information to be archived in DNA molecules. This method enjoys major advantages over magnetic and optical storage solutions such as exceptional information density, enhanced data durability, and negligible power consumption to maintain data integrity. To access the data, an information retrieval process is employed, where some of the main bottlenecks are the scalability and accuracy, which have a natural tradeoff between the two. Here we show a modular and holistic approach that combines Deep Neural Networks (DNN) trained on simulated data, Tensor-Product (TP) based Error-Correcting Codes (ECC), and a safety margin mechanism into a single coherent pipeline. We demonstrated our solution on 3.1MB of information using two different sequencing technologies. Our work improves upon the current leading solutions by up to x3200 increase in speed, 40% improvement in accuracy, and offers a code rate of 1.6 bits per base in a high noise regime. In a broader sense, our work shows a viable path to commercial DNA storage solutions hindered by current information retrieval processes.