High-density information storage and random access scheme using synthetic DNA

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The high-storage density, long-life cycle, and low-energy consumption of DNA molecules make it the future of next-generation storage technology. However, DNA storage has the disadvantages of high-synthesis cost and low-random access efficiency. A high-density DNA-coding scheme can effectively reduce the cost of DNA synthesis. This paper first proposes a DNA-mapping method based on codebook and a random access method for DNA information based on encoded content. The mapping method satisfies the two biological constraints of homopolymer length and GC content. The random access method can efficiently and selectively read specific files in the DNA pool. To increase storage density, convolutional neural networks are combined with mapping methods to generate base sequences. In the experiments, our method was compared with the results of existing DNA information storage methods, which showed that the proposed scheme has better information storage density.