Rewritable two-dimensional DNA-based data storage with machine learning reconstruction

Chao Pan, S. Kasra Tabatabaei, S. M. Hossein Tabatabaei Yazdi, Alvaro G. Hernandez, Charles M. Schroeder () and Olgica Milenkovic ()
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Chao Pan: University of Illinois at Urbana-Champaign
S. Kasra Tabatabaei: University of Illinois at Urbana-Champaign
S. M. Hossein Tabatabaei Yazdi: Dorna Robotics
Alvaro G. Hernandez: University of Illinois at Urbana-Champaign
Charles M. Schroeder: University of Illinois at Urbana-Champaign
Olgica Milenkovic: University of Illinois at Urbana-Champaign

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: 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 and performs nontrivial joint data encoding, decoding and processing. Our 2DDNA method efficiently stores images in synthetic DNA and embeds pertinent metadata as nicks in the DNA backbone. To avoid costly worst-case redundancy for correcting sequencing/rewriting errors and to mitigate issues associated with mismatched decoding parameters, we develop machine learning techniques for automatic discoloration detection and image inpainting. The 2DDNA platform is experimentally tested by reconstructing a library of images with undetectable or small visual degradation after readout processing, and by erasing and rewriting copyright metadata encoded in nicks. Our results demonstrate that DNA can serve both as a write-once and rewritable memory for heterogenous data and that data can be erased in a permanent, privacy-preserving manner. Moreover, the storage system can be made robust to degrading channel qualities while avoiding global error-correction redundancy.

Date: 2022
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DOI: 10.1038/s41467-022-30140-x

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