Rewritable Two-Dimensional DNA-Based Data Storage with Machine Learning Reconstruction

Abstract

DNA-based data storage platforms traditionally encode information only in the nucleotide sequence of the molecule. Here, we report on a two-dimensional molecular data storage system that records information in both the sequence and the backbone structure of DNA. Our “2DDNA” method efficiently stores high-density images in synthetic DNA and embeds metadata as nicks in the DNA backbone. To avoid costly redundancy used to combat sequencing errors and missing information content that typically requires additional synthesis, specialized machine learning methods are developed for automatic discoloration detection and image inpainting. The 2DDNA platform is experimentally tested on a library of images that show undetectable visual degradation after processing, while the image metadata is erased and rewritten to modify copyright information. Our results show that DNA can serve both as a write-once and rewritable memory for heterogenous data. Moreover, the storage density of the molecules can be increased by using different encoding dimensions and avoiding error-correction redundancy.