Deep DNA Storage: Scalable and Robust DNA Storage via Coding Theory and Deep Learning

• Published in: arXiv.org
• Main Authors: Bar-Lev, Daniella, Orr, Itai, Sabary, Omer, Etzion, Tuvi, Yaakobi, Eitan
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 11.03.2024
• Subjects: Artificial neural networks; Clustering; Codes; Deoxyribonucleic acid; DNA; Error correcting codes; Error correction; Gene sequencing; Information retrieval; Machine learning; Nanotechnology; Robustness (mathematics); Storage systems; Synthesis

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.