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DNA assembly for nanopore data storage readout

Author

Listed:
  • Randolph Lopez

    (University of Washington
    University of Washington)

  • Yuan-Jyue Chen

    (Microsoft Research)

  • Siena Dumas Ang

    (Microsoft Research)

  • Sergey Yekhanin

    (Microsoft Research)

  • Konstantin Makarychev

    (Microsoft Research)

  • Miklos Z Racz

    (Microsoft Research)

  • Georg Seelig

    (University of Washington
    University of Washington
    University of Washington)

  • Karin Strauss

    (Microsoft Research)

  • Luis Ceze

    (University of Washington)

Abstract

Synthetic DNA is becoming an attractive substrate for digital data storage due to its density, durability, and relevance in biological research. A major challenge in making DNA data storage a reality is that reading DNA back into data using sequencing by synthesis remains a laborious, slow and expensive process. Here, we demonstrate successful decoding of 1.67 megabytes of information stored in short fragments of synthetic DNA using a portable nanopore sequencing platform. We design and validate an assembly strategy for DNA storage that drastically increases the throughput of nanopore sequencing. Importantly, this assembly strategy is generalizable to any application that requires nanopore sequencing of small DNA amplicons.

Suggested Citation

  • Randolph Lopez & Yuan-Jyue Chen & Siena Dumas Ang & Sergey Yekhanin & Konstantin Makarychev & Miklos Z Racz & Georg Seelig & Karin Strauss & Luis Ceze, 2019. "DNA assembly for nanopore data storage readout," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10978-4
    DOI: 10.1038/s41467-019-10978-4
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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.
    2. Andreas L. Gimpel & Wendelin J. Stark & Reinhard Heckel & Robert N. Grass, 2023. "A digital twin for DNA data storage based on comprehensive quantification of errors and biases," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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