Towards scalable DNA data storage with DNA-SaM codec system

The exponential growth of global data volume is driving an urgent need for novel storage medium. Deoxyribonucleic Acid (DNA), nature’s genetic repository, presents a transformative solution with data storage density and lifespan orders of magnitude higher than conventional media. However, scaling DNA storage has been hindered by the complexity of codec algorithms, the generation of biochemically problematic sequences, and the absence of robust architecture for large-scale data. We present DNA-SaM, a system integrating highly efficient encoding, stringent biochemical constraint checks, and a storage unit distribution paradigm facilitated by a two-tiered indexing architecture. DNA-SaM demonstrates linear encoding time complexity and substantially reduced encoding time compared with representative approaches by in silico analysis. Critically, it proactively excludes sequences such as homopolymer runs, tandem repeats, and potential promoter motifs that could interfere with downstream biochemical processes, showing improved compatibility with in vitro and in vivo applications. It is further validated by practical data storage both in vitro and in vivo with a 100% success rate. On the basis of its encoding scheme and sequence design space, DNA-SaM could theoretically store data exceeding 270 petabytes (PB), marking an advancement in the scalability of DNA-based data storage. Scaling DNA storage is limited by complex codecs, biochemically problematic sequences, and insufficient architecture for large-scale data. Here, the authors present DNA-SaM, integrating efficient encoding, biochemical checks, and a storage unit distribution with a two-tiered indexing architecture.