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Scalable and robust DNA-based storage via coding theory and deep learning

Nature Machine Intelligence volume 7, pages 639–649 (2025)Cite this article

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A preprint version of the article is available at arXiv.

Abstract

The global data sphere is expanding exponentially, projected to hit 180 zettabytes by 2025, whereas current technologies are not anticipated to scale at nearly the same rate. DNA-based storage emerges as a crucial solution to this gap, enabling digital information to be archived in DNA molecules. This method enjoys major advantages over magnetic and optical storage solutions such as exceptional information density, enhanced data durability and negligible power consumption to maintain data integrity. To access the data, an information retrieval process is employed, where some of the main bottlenecks are the scalability and accuracy, which have a natural tradeoff between the two. Here we show a modular and holistic approach that combines deep neural networks trained on simulated data, tensor product-based error-correcting codes and a safety margin mechanism into a single coherent pipeline. We demonstrated our solution on 3.1 MB of information using two different sequencing technologies. Our work improves upon the current leading solutions with a 3,200× increase in speed and a 40% improvement in accuracy and offers a code rate of 1.6 bits per base in a high-noise regime. In a broader sense, our work shows a viable path to commercial DNA storage solutions hindered by current information retrieval processes.

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Fig. 1: End-to-end solution for DNA information retrieval.
Fig. 2: Data used for DNA experiments.
Fig. 3: Comparison of the DNAformer with SOTA DNA reconstruction methods.
Fig. 4: Evaluation of information retrieval performance.

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Data availability

The datasets created and discussed in this Article are available via Zenodo at https://zenodo.org/records/13896773 (ref. 50). The synthetic data generator used to create the simulated training data is available in the data generator folder of our GitHub repository at https://github.com/itaiorr/Deep-DNA-based-storage.git (ref. 51). The publicly available datasets used in this study are sourced from the following publications: Grass et al.13,52, available via Zenodo at https://zenodo.org/records/14290755 (ref. 53); Erlich et al.15, available at http://www.ebi.ac.uk/ena/data/view/PRJEB19305 and http://www.ebi.ac.uk/ena/data/view/PRJEB19307; Organick et al.16, available at http://misl.cs.washington.edu/data and https://github.com/uwmisl/data-nbt17; and Srinivasavaradhan et al.7, available via GitHub at https://github.com/microsoft/clustered-nanopore-reads-dataset. The datasets from refs. 7,13,15, where the reads are classified into clusters by using our binning algorithm, are available via Zenodo at https://zenodo.org/records/14296588 (ref. 54). Binning of the dataset from ref. 16 is possible by applying the preprocessing and binning scripts (reads_preprocessor.py and binning.py) available via GitHub at https://github.com/itaiorr/Deep-DNA-based-storage.git (ref. 51). A parametric comparison of previous DNA data storage experiments55,56,57, with additional parameters related to our data, is provided in Supplementary Table 4.

Code availability

The code used for this work is available via GitHub at https://github.com/itaiorr/Deep-DNA-based-storage.git (ref. 51).

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Acknowledgements

D.B.-L., I.O., O.S. and E.Y. were Funded by the European Union (ERC, DNAStorage, 101045114 and EIC, DiDAX 101115134). The views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. D.B.-L. and T.E. were supported in part by ISF grant no. 222/19. The authors thank S. Goldberg, N. Kikuchi and R. Amit for assistance with the wet experiments and providing the lab and equipment; N. Fourier and L. Linde for their help in the sequencing processes and D. B. Shabat for his assistance and advice. Finally, we thank A. Yucovich for his help with developing the CPL algorithm.

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Author notes

  1. These authors contributed equally: Daniella Bar-Lev, Itai Orr, Omer Sabary.

Authors and Affiliations

  1. Computer Science Faculty, Technion–Israel Institute of Technology, Haifa, Israel

    Daniella Bar-Lev, Itai Orr, Omer Sabary, Tuvi Etzion & Eitan Yaakobi

  2. UVeye Ltd., Tel Aviv, Israel

    Itai Orr

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Contributions

Conceptualization: D.B.-L., I.O. and O.S. Methodology: D.B.-L., I.O. and O.S. Investigation: D.B.-L., I.O., O.S., T.E. and E.Y. Supervision: T.E. and E.Y. Writing, review and editing: D.B.-L., I.O., O.S., T.E. and E.Y.

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Correspondence to Daniella Bar-Lev, Itai Orr or Omer Sabary.

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Competing interests

D.B.-L., I.O., O.S., T.E. and E.Y. are inventors on patent application US 18/233,855 submitted by the Technion–Israel Institute of Technology and Bar Ilan University.

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Supplementary Results, Figs. 1–13 and Tables 1–10.

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Bar-Lev, D., Orr, I., Sabary, O. et al. Scalable and robust DNA-based storage via coding theory and deep learning. Nat Mach Intell 7, 639–649 (2025). https://doi.org/10.1038/s42256-025-01003-z

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