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<p>DNA has emerged as a compelling archival storage medium, offering unprecedented information density and millennia-scale durability. Despite its promise, DNA-based data storage faces critical challenges due to error-prone processes during DNA synthesis, storage, and sequencing. In this study, we introduce Gungnir, a codec system using the proof-of-work idea to address substitution, insertion, and deletion errors in a sequence. With a hash signature for each data fragment, Gungnir corrects the errors by testing the educated guesses until the hash signature is matched. For practicality, especially when sequenced with nanopore long-read, Gungnir also considers biochemical constraints, including GC-content, homopolymers, and error-prone motifs during encoding. In silico benchmarking demonstrates its outperforming error resilience capacity against the state-of-the-art methods and achieving complete binary data recovery from a single sequence copy containing 20% erroneous bases. Gungnir requires neither keeping many redundant sequence copies to address molecular decay in archival storage, nor high-coverage sequencing to address sequencing error, reducing the overall cost of using DNA for storage.</p>

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Gungnir codec enabling high error-tolerance and low-redundancy DNA storage through substantial computing power

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月4日

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  • Jingcheng Zhang,
  • Lei Chen,
  • Jinlin Sun,
  • Shumin Li,
  • Yekai Zhou,
  • Zhenqin Wu,
  • Can Li,
  • Zhenxian Zheng,
  • Ruibang Luo

摘要

DNA has emerged as a compelling archival storage medium, offering unprecedented information density and millennia-scale durability. Despite its promise, DNA-based data storage faces critical challenges due to error-prone processes during DNA synthesis, storage, and sequencing. In this study, we introduce Gungnir, a codec system using the proof-of-work idea to address substitution, insertion, and deletion errors in a sequence. With a hash signature for each data fragment, Gungnir corrects the errors by testing the educated guesses until the hash signature is matched. For practicality, especially when sequenced with nanopore long-read, Gungnir also considers biochemical constraints, including GC-content, homopolymers, and error-prone motifs during encoding. In silico benchmarking demonstrates its outperforming error resilience capacity against the state-of-the-art methods and achieving complete binary data recovery from a single sequence copy containing 20% erroneous bases. Gungnir requires neither keeping many redundant sequence copies to address molecular decay in archival storage, nor high-coverage sequencing to address sequencing error, reducing the overall cost of using DNA for storage.