Toward sustainable DNA storage: Adaptive error-resilient encoding under biochemical constraints

Synthetic DNA is increasingly recognized as a promising medium for sustainable digital data archiving, offering exceptional information density and long-term durability. However, practical implementation faces significant challenges due to the biochemical constraints of DNA molecules, such as the need for balanced GC content, avoidance of secondary structures, and resilience to synthesis and sequencing errors. In this work, we present an adaptive, error-resilient encoding protocol that integrates algebraic coding techniques with sequence-level biochemical constraints. The proposed protocol utilizes linear-complexity algorithms to encode digital data into DNA sequences while precisely controlling GC content and maximizing reverse complement distance to enhance molecular stability. To ensure data integrity, the scheme systematically corrects single-edit errors using algebraic structures with homomorphic properties in conjunction with the generalized Varshamov–Tenengolts algorithm. By using the kernel of these homomorphisms, our approach simultaneously achieves effective error correction and GC-content balancing. Experimental results across various codeword lengths, including simulations with introduced errors, demonstrate that the protocol maintains low byte error rates while satisfying strict biochemical constraints. These results highlight the protocol’s potential as a robust and sustainable solution for future DNA-based data storage systems.