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DNA-Aeon provides flexible arithmetic coding for constraint adherence and error correction in DNA storage

Author

Listed:
  • Marius Welzel

    (University of Marburg
    University of Marburg)

  • Peter Michael Schwarz

    (University of Marburg
    University of Marburg)

  • Hannah F. Löchel

    (University of Marburg
    University of Marburg)

  • Tolganay Kabdullayeva

    (University of Marburg)

  • Sandra Clemens

    (University of Marburg
    University of Marburg)

  • Anke Becker

    (University of Marburg)

  • Bernd Freisleben

    (University of Marburg
    University of Marburg)

  • Dominik Heider

    (University of Marburg
    University of Marburg)

Abstract

The extensive information capacity of DNA, coupled with decreasing costs for DNA synthesis and sequencing, makes DNA an attractive alternative to traditional data storage. The processes of writing, storing, and reading DNA exhibit specific error profiles and constraints DNA sequences have to adhere to. We present DNA-Aeon, a concatenated coding scheme for DNA data storage. It supports the generation of variable-sized encoded sequences with a user-defined Guanine-Cytosine (GC) content, homopolymer length limitation, and the avoidance of undesired motifs. It further enables users to provide custom codebooks adhering to further constraints. DNA-Aeon can correct substitution errors, insertions, deletions, and the loss of whole DNA strands. Comparisons with other codes show better error-correction capabilities of DNA-Aeon at similar redundancy levels with decreased DNA synthesis costs. In-vitro tests indicate high reliability of DNA-Aeon even in the case of skewed sequencing read distributions and high read-dropout.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36297-3
    DOI: 10.1038/s41467-023-36297-3
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Karishma Matange & James M. Tuck & Albert J. Keung, 2021. "DNA stability: a central design consideration for DNA data storage systems," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
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