Noise-immune and AI-enhanced DNA storage via adaptive partition mapping of digital data

TL;DR

This paper introduces a novel DNA storage encoding scheme called PJ that significantly enhances noise resilience, enabling reliable data retrieval even under severe strand loss and environmental disturbances, leveraging AI inference.

Contribution

The paper presents the PJ encoding scheme with jump-rotating intra-strand encoding, improving noise tolerance and decodability in DNA storage beyond traditional methods.

Findings

Successful recovery of files with 10% strand loss

Effective decoding of images after environmental stress

Robustness demonstrated under accelerated aging and X-ray irradiation

Abstract

Encoding digital information into DNA sequences offers an attractive potential solution for storing rapidly growing data under the information age and the rise of artificial intelligence. However, practical implementations of DNA storage are constrained by errors introduced during synthesis, preservation, and sequencing processes, and traditional error-correcting codes remain vulnerable to noise levels that exceed predefined thresholds. Here, we developed a Partitioning-mapping with Jump-rotating (PJ) encoding scheme, which exhibits exceptional noise resilience. PJ removes cross-strand information dependencies so that strand loss manifests as localized gaps rather than catastrophic file failure. It prioritizes file decodability under arbitrary noise conditions and leverages AI-based inference to enable controllable recovery of digital information. For the intra-strand encoding, we develop a jump-rotating strategy that relaxes sequence constraints relative to conventional rotating codes and provides tunable information density via an adjustable jump length. Based on this encoding architecture, the original file information can always be decoded and recovered under any strand loss ratio, with fidelity degrading smoothly as damage increases. We demonstrate that original files can be effectively recovered even with 10% strand loss, and machine learning datasets stored under these conditions retain their classification performance. Experiments further confirmed that PJ successfully decodes image files after extreme environmental disturbance using accelerated aging and high-intensity X-ray irradiation. By eliminating reliance on prior error probabilities, PJ establishes a general framework for robust, archival DNA storage capable of withstanding the rigorous conditions of real-world preservation.