IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-64230-3.html
   My bibliography  Save this article

DNA StairLoop: enabling high-fidelity data recovery and robust error correction in DNA-based data storage

Author

Listed:
  • Zihui Yan

    (Tianjin University
    Tianjin University
    Tianjin University
    Tianjin University)

  • Guanjin Qu

    (Tianjin University)

  • Xin Chen

    (Tianjin University
    Tianjin University)

  • Gang Zheng

    (National Supercomputer Center in Tianjin)

  • Huaming Wu

    (Tianjin University
    Tianjin University)

Abstract

High-throughput electrochemical synthesis is an emerging DNA synthesis technology that has attracted considerable attention due to its scalability and cost-effectiveness. However, its high error rates and poor synthesis uniformity pose significant challenges for applications in DNA data storage, where high fidelity is essential. In this study, we present StairLoop, a coding scheme designed to address these error characteristics, providing robust error-correcting capabilities. Validated through in-vitro experiments, StairLoop successfully recovers original data under harsh conditions, including nucleotide error rates exceeding 6% or dropout rates over 30% within a block, with sequencing depths of less than 3x . Moreover, the simulation results show that StairLoop can achieve an error correction capability of 10% at the mean coverage rate of 15x. These results highlight StairLoop’s potential to enhance the reliability and practicality of electrochemical DNA synthesis for data storage applications.

Suggested Citation

  • Zihui Yan & Guanjin Qu & Xin Chen & Gang Zheng & Huaming Wu, 2025. "DNA StairLoop: enabling high-fidelity data recovery and robust error correction in DNA-based data storage," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-64230-3
    DOI: 10.1038/s41467-025-64230-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-64230-3
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-025-64230-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Philipp L. Antkowiak & Jory Lietard & Mohammad Zalbagi Darestani & Mark M. Somoza & Wendelin J. Stark & Reinhard Heckel & Robert N. Grass, 2020. "Low cost DNA data storage using photolithographic synthesis and advanced information reconstruction and error correction," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    3. Xuyang Zhao & Junyao Li & Qingyuan Fan & Jing Dai & Yanping Long & Ronghui Liu & Jixian Zhai & Qing Pan & Yi Li, 2024. "Composite Hedges Nanopores codec system for rapid and portable DNA data readout with high INDEL-Correction," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    4. Jay Shendure & Shankar Balasubramanian & George M. Church & Walter Gilbert & Jane Rogers & Jeffery A. Schloss & Robert H. Waterston, 2017. "DNA sequencing at 40: past, present and future," Nature, Nature, vol. 550(7676), pages 345-353, October.
    5. 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.
    6. 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.
    7. Lifu Song & Feng Geng & Zi-Yi Gong & Xin Chen & Jijun Tang & Chunye Gong & Libang Zhou & Rui Xia & Ming-Zhe Han & Jing-Yi Xu & Bing-Zhi Li & Ying-Jin Yuan, 2022. "Robust data storage in DNA by de Bruijn graph-based de novo strand assembly," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cheng Kai Lim & Jing Wui Yeoh & Aurelius Andrew Kunartama & Wen Shan Yew & Chueh Loo Poh, 2023. "A biological camera that captures and stores images directly into DNA," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Afsaneh Sadremomtaz & Robert F. Glass & Jorge Eduardo Guerrero & Dennis R. LaJeunesse & Eric A. Josephs & Reza Zadegan, 2023. "Digital data storage on DNA tape using CRISPR base editors," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    3. Qingyuan Fan & Xuyang Zhao & Junyao Li & Ronghui Liu & Ming Liu & Qishun Feng & Yanping Long & Yang Fu & Jixian Zhai & Qing Pan & Yi Li, 2025. "De novo non-canonical nanopore basecalling enables private communication using heavily-modified DNA data at single-molecule level," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    4. Isak S. Pretorius & Thomas A. Dixon & Michael Boers & Ian T. Paulsen & Daniel L. Johnson, 2025. "The coming wave of confluent biosynthetic, bioinformational and bioengineering technologies," Nature Communications, Nature, vol. 16(1), pages 1-8, December.
    5. Carina Imburgia & Lee Organick & Karen Zhang & Nicolas Cardozo & Jeff McBride & Callista Bee & Delaney Wilde & Gwendolin Roote & Sophia Jorgensen & David Ward & Charlie Anderson & Karin Strauss & Luis, 2025. "Random access and semantic search in DNA data storage enabled by Cas9 and machine-guided design," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    6. Christoph Ziegenhain & Rickard Sandberg, 2021. "BAMboozle removes genetic variation from human sequence data for open data sharing," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    7. Abdur Rasool & Qiang Qu & Yang Wang & Qingshan Jiang, 2022. "Bio-Constrained Codes with Neural Network for Density-Based DNA Data Storage," Mathematics, MDPI, vol. 10(5), pages 1-21, March.
    8. Christopher N. Takahashi & David P. Ward & Carlo Cazzaniga & Christopher Frost & Paolo Rech & Kumkum Ganguly & Sean Blanchard & Steve Wender & Bichlien H. Nguyen & Jake A. Smith, 2024. "Evaluating the risk of data loss due to particle radiation damage in a DNA data storage system," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    9. Susanne Theuerl & Christiane Herrmann & Monika Heiermann & Philipp Grundmann & Niels Landwehr & Ulrich Kreidenweis & Annette Prochnow, 2019. "The Future Agricultural Biogas Plant in Germany: A Vision," Energies, MDPI, vol. 12(3), pages 1-32, January.
    10. Tony Z. Jia & Yutetsu Kuruma, 2019. "Recent Advances in Origins of Life Research by Biophysicists in Japan," Challenges, MDPI, vol. 10(1), pages 1-21, April.
    11. Hinako Kawabe & Christopher A. Thomas & Shuichi Hoshika & Myong-Jung Kim & Myong-Sang Kim & Logan Miessner & Nicholas Kaplan & Jonathan M. Craig & Jens H. Gundlach & Andrew H. Laszlo & Steven A. Benne, 2023. "Enzymatic synthesis and nanopore sequencing of 12-letter supernumerary DNA," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    12. Mohammed Alser & Julien Eudine & Onur Mutlu, 2025. "Taming large-scale genomic analyses via sparsified genomics," Nature Communications, Nature, vol. 16(1), pages 1-21, December.
    13. Junyao Li & Xuyang Zhao & Qingyuan Fan & Yanping Long & Ronghui Liu & Jixian Zhai & Qing Pan & Yi Li, 2025. "Empowering low-crosstalk, dynamic-decision random access of DNA storage via 384-multiplexed nanopore signatures," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    14. Hannes Rothe & Katharina Barbara Lauer & Callum Talbot-Cooper & Daniel Juan Sivizaca Conde, 2023. "Digital entrepreneurship from cellular data: How omics afford the emergence of a new wave of digital ventures in health," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-17, December.
    15. 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.
    16. Christopher A. Thomas & Henry Brinkerhoff & Jonathan M. Craig & Shuichi Hoshika & Desislava Mihaylova & Akira M. Pfeffer & Michaela C. Franzi & Sarah J. Abell & Jessica D. Carrasco & Jens H. Gundlach , 2025. "Sequencing a DNA analog composed of artificial bases," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    17. Jingwei Hong & Abdur Rasool & Shuo Wang & Djemel Ziou & Qingshan Jiang, 2024. "VSD: A Novel Method for Video Segmentation and Storage in DNA Using RS Code," Mathematics, MDPI, vol. 12(8), pages 1-21, April.
    18. Andreas L. Gimpel & Bowen Fan & Dexiong Chen & Laetitia O. D. Wölfle & Max Horn & Laetitia Meng-Papaxanthos & Philipp L. Antkowiak & Wendelin J. Stark & Beat Christen & Karsten Borgwardt & Robert N. G, 2025. "Predicting sequence-specific amplification efficiency in multi-template PCR with deep learning," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    19. Susanne Theuerl & Johanna Klang & Annette Prochnow, 2019. "Process Disturbances in Agricultural Biogas Production—Causes, Mechanisms and Effects on the Biogas Microbiome: A Review," Energies, MDPI, vol. 12(3), pages 1-20, January.
    20. Mauricio Perez & Michiko Kimoto & Priscilla Rajakumar & Chayaporn Suphavilai & Rafael Peres da Silva & Hui Pen Tan & Nicholas Ting Xun Ong & Hannah Nicholas & Ichiro Hirao & Wei Leong Chew & Niranjan , 2025. "Direct high-throughput deconvolution of non-canonical bases via nanopore sequencing and bootstrapped learning," Nature Communications, Nature, vol. 16(1), pages 1-12, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-64230-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.