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; Olgica Milenkovic

doi:10.1038/s41467-022-30140-x

Nature Communications (May 2022)

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,
Olgica Milenkovic

Affiliations

Chao Pan: Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
S. Kasra Tabatabaei: Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign
S. M. Hossein Tabatabaei Yazdi: Dorna Robotics
Alvaro G. Hernandez: Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign
Charles M. Schroeder: Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign
Olgica Milenkovic: Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign

DOI: https://doi.org/10.1038/s41467-022-30140-x
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 12

Abstract

Read online

Current DNA-based data storage platforms encode information only in the nucleotide sequence. Here, the authors report a 2DDNA platform that can store data in both sequence context and backbone structure, and has improved image inpainting and enhancement via automatic discoloration detection and deep learning.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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