Learning Structurally Stabilized Representations for Multi-modal Lossless DNA Storage
This work addresses the challenge of efficient and reliable data storage in DNA for applications in biotechnology and data archiving, representing an incremental improvement over existing learning-based methods.
The paper tackles the problem of learning durable, dense, and lossless representations for multi-modal DNA storage by proposing RSRL, which integrates error-correction coding and structural biology to achieve higher information density and durability with lower error rates compared to baselines.
In this paper, we present Reed-Solomon coded single-stranded representation learning (RSRL), a novel end-to-end model for learning representations for multi-modal lossless DNA storage. In contrast to existing learning-based methods, the proposed RSRL is inspired by both error-correction codec and structural biology. Specifically, RSRL first learns the representations for the subsequent storage from the binary data transformed by the Reed-Solomon codec. Then, the representations are masked by an RS-code-informed mask to focus on correcting the burst errors occurring in the learning process. With the decoded representations with error corrections, a novel biologically stabilized loss is formulated to regularize the data representations to possess stable single-stranded structures. By incorporating these novel strategies, the proposed RSRL can learn highly durable, dense, and lossless representations for the subsequent storage tasks into DNA sequences. The proposed RSRL has been compared with a number of strong baselines in real-world tasks of multi-modal data storage. The experimental results obtained demonstrate that RSRL can store diverse types of data with much higher information density and durability but much lower error rates.