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

View ORCID ProfileChao Pan, View ORCID ProfileS. Kasra Tabatabaei, View ORCID ProfileSM Hossein Tabatabaei Yazdi, View ORCID ProfileAlvaro G. Hernandez, View ORCID ProfileCharles M. Schroeder, View ORCID ProfileOlgica Milenkovic
doi: https://doi.org/10.1101/2021.02.22.432304
Chao Pan
1Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
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  • ORCID record for Chao Pan
S. Kasra Tabatabaei
2Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
3Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
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SM Hossein Tabatabaei Yazdi
5Dorna Robotics, Upland, CA, 91786, USA
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  • ORCID record for SM Hossein Tabatabaei Yazdi
Alvaro G. Hernandez
6Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
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Charles M. Schroeder
2Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
3Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
4Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
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  • For correspondence: cms@illinois.edu milenkov@illinois.edu
Olgica Milenkovic
1Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
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  • ORCID record for Olgica Milenkovic
  • For correspondence: cms@illinois.edu milenkov@illinois.edu
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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.

Competing Interest Statement

The work was funded by the DARPA Molecular Informatics Program, the NSF+SRC SemiSynBio program under agreement number 1807526 and the NSF grant 1618366. University of Illinois at Urbana-Champaign has filed a (pending) nonprovisional patent on behalf of C.P., O.M., C.M.S., SMH.T.Y., A.G.H. and SK.T., with application number: 17/102,143.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted February 23, 2021.
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Rewritable Two-Dimensional DNA-Based Data Storage with Machine Learning Reconstruction
Chao Pan, S. Kasra Tabatabaei, SM Hossein Tabatabaei Yazdi, Alvaro G. Hernandez, Charles M. Schroeder, Olgica Milenkovic
bioRxiv 2021.02.22.432304; doi: https://doi.org/10.1101/2021.02.22.432304
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Rewritable Two-Dimensional DNA-Based Data Storage with Machine Learning Reconstruction
Chao Pan, S. Kasra Tabatabaei, SM Hossein Tabatabaei Yazdi, Alvaro G. Hernandez, Charles M. Schroeder, Olgica Milenkovic
bioRxiv 2021.02.22.432304; doi: https://doi.org/10.1101/2021.02.22.432304

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