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

Author

Listed:
  • Chao Pan

    (University of Illinois at Urbana-Champaign)

  • S. Kasra Tabatabaei

    (University of Illinois at Urbana-Champaign
    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
    University of Illinois at Urbana-Champaign
    University of Illinois at Urbana-Champaign)

  • Olgica Milenkovic

    (University of Illinois at Urbana-Champaign)

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.

Suggested Citation

  • Chao Pan & S. Kasra Tabatabaei & S. M. Hossein Tabatabaei Yazdi & Alvaro G. Hernandez & Charles M. Schroeder & Olgica Milenkovic, 2022. "Rewritable two-dimensional DNA-based data storage with machine learning reconstruction," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30140-x
    DOI: 10.1038/s41467-022-30140-x
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    References listed on IDEAS

    as
    1. Nick Goldman & Paul Bertone & Siyuan Chen & Christophe Dessimoz & Emily M. LeProust & Botond Sipos & Ewan Birney, 2013. "Towards practical, high-capacity, low-maintenance information storage in synthesized DNA," Nature, Nature, vol. 494(7435), pages 77-80, February.
    2. Christopher E. Arcadia & Eamonn Kennedy & Joseph Geiser & Amanda Dombroski & Kady Oakley & Shui-Ling Chen & Leonard Sprague & Mustafa Ozmen & Jason Sello & Peter M. Weber & Sherief Reda & Christopher , 2020. "Multicomponent molecular memory," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    3. S. Kasra Tabatabaei & Boya Wang & Nagendra Bala Murali Athreya & Behnam Enghiad & Alvaro Gonzalo Hernandez & Christopher J. Fields & Jean-Pierre Leburton & David Soloveichik & Huimin Zhao & Olgica Mil, 2020. "DNA punch cards for storing data on native DNA sequences via enzymatic nicking," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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    Cited by:

    1. Marius Welzel & Peter Michael Schwarz & Hannah F. Löchel & Tolganay Kabdullayeva & Sandra Clemens & Anke Becker & Bernd Freisleben & Dominik Heider, 2023. "DNA-Aeon provides flexible arithmetic coding for constraint adherence and error correction in DNA storage," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

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