DINOS: Data INspired Oligo Synthesis for DNA Data Storage

As interest in DNA-based information storage grows, the costs of synthesis have been identified as a key bottleneck. A potential direction is to tune for data. Data strands tend be composed small set recurring code word sequences, and they contain longer sequences repeated To exploit these properties, we propose new framework called DINOS. DINOS consists three parts: (i) The first hierarchical strand assembly algorithm, inspired by gene techniques that can assemble arbitrary data from primitive blocks. (ii) algorithm relies on our novel formulation how construct blocks, spanning variety useful configurations words overhangs. Each block flanked pair overhangs are created cyclic pairing process keeps number blocks small. Using any length assembled, theoretically. We show minimal system binary with few six generalize processes support an words. (iii) approach identify redundant coalesce reactions create them make more efficient. evaluate describe its characteristics. For example, needed reduced increasing or words, but offers advantage over while requiring substantially fewer However, density improved also find simple redundancy coalescing technique able reduce 90.6% 41.2% average decompressed compressed data, respectively, even when smallest fragments being assembled 16 bits. With padding heuristic finds redundancy, further decrease same operating point up 91.1% 59% average. Our greater 80% prior general purpose technique. Finally, analysis which 1 GB volume using de novo versus making only otherwise assembling DINOS, estimate 10 5 × cheaper than synthesis.