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DNA data storage for biomedical images using HELIX

Abstract

Deoxyribonucleic acid (DNA) data storage is expected to become a key medium for large-scale data. Biomedical data images typically require substantial storage space over extended periods, making them ideal candidates for DNA data storage. However, existing DNA data storage models are primarily designed for generic files and lack a comprehensive retrieval system for biomedical images. Here, to address this, we propose HELIX, a DNA-based storage system for biomedical images. HELIX introduces an image-compression algorithm tailored to the characteristics of biomedical images, achieving high compression rates and robust error tolerance. In addition, HELIX incorporates an error-correcting encoding algorithm that eliminates the need for indexing, enhancing storage density and decoding speed. We utilize a deep learning-based image repair algorithm for the predictive restoration of partially missing image blocks. In our in vitro experiments, we successfully stored two spatiotemporal genomics images. This sequencing process achieved 97.20% image quality at a depth of 7× coverage.

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Fig. 1: The overall modeling framework of HELIX.
Fig. 2: Fault-tolerance performance of different image-compression algorithms.
Fig. 3: Evaluation of the effectiveness of the HELIX simulation experiment.
Fig. 4: Flowchart of in vitro experiments.

Data availability

Sequencing data from in vitro experiments can be found at https://doi.org/10.6084/m9.figshare.25957606.v1 (ref. 32). The images used in the HELIX test are all from publicly available datasets. The spatiotemporalomics image dataset is from ref. 21. The computed tomography image dataset is from https://doi.org/10.7937/K9/TCIA.2016.JGNIHEP5 (ref. 23). The X-ray image dataset is from https://doi.org/10.17632/t9ndx37v5h.1 (ref. 22).

Code availability

HELIX includes both Go and Python versions. The code can be found at https://doi.org/10.5281/zenodo.14699789 (ref. 33).

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (number 2020YFA0712100) and the National Natural Science Foundation of China (number 62071327).

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Authors and Affiliations

  1. Center for Applied Mathematics, Tianjin University, Tianjin, P. R. China

    Guanjin Qu, Xin Chen & Huaming Wu

  2. School of Chemical Engineering and Technology, Tianjin University, Tianjin, P. R. China

    Zihui Yan

  3. State Key Laboratory of Synthetic Biology, Tianjin University, Tianjin, P. R. China

    Zihui Yan, Xin Chen & Huaming Wu

Authors

  1. Guanjin Qu
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  2. Zihui Yan
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  3. Xin Chen
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  4. Huaming Wu
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Contributions

H.W. and X.C. conceived of and supervised the project. G.Q. derived analytical results and performed numerical calculations. G.Q. and Z.Y. analyzed the data. G.Q. wrote the original draft, and H.W. and Z.Y. reviewed and edited it. All authors read and approved the final paper.

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Correspondence to
Huaming Wu.

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Nature Computational Science thanks Zhi Ping, Leyi Wei and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ananya Rastogi, in collaboration with the Nature Computational Science team.

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Cite this article

Qu, G., Yan, Z., Chen, X. et al. DNA data storage for biomedical images using HELIX.
Nat Comput Sci (2025). https://doi.org/10.1038/s43588-025-00793-x

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  • Received: 06 June 2024

  • Accepted: 18 March 2025

  • Published: 13 May 2025

  • DOI: https://doi.org/10.1038/s43588-025-00793-x

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