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RFS-codec: A Novel Encoding Approach to Store Image Data in DNA
Research Article | Published: 30 June 2025
Journal of Artificial Intelligence in Bioinformatics Volume 1, Issue 1, 2025: 41-50
Volume 1, Issue 1, Journal of Artificial Intelligence in Bioinformatics
Volume 1, Issue 1, 2025
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Journal of Artificial Intelligence in Bioinformatics, Volume 1, Issue 1, 2025: 41-50

Open Access | Research Article | 30 June 2025
RFS-codec: A Novel Encoding Approach to Store Image Data in DNA
by
Abdur Rasool Abdur Rasool 1 *
1 Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, United States
* Corresponding Author: Abdur Rasool, [email protected]
DOI: 10.62762/JAIB.2025.146324
Received: 15 May 2025, Accepted: 22 June 2025, Published: 30 June 2025  
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Abstract
DNA data storage is a promising technology that utilizes computer simulation and offers high-density and durable digital information storage. It is challenging to store massive image data in a small amount of DNA without losing the original data since nonspecific hybridization errors occur frequently and severely affect the durability of stored data. This work proposes a novel approach (RFS-codec) comprising an image fraction strategy and an innovative codec method to split and encode image data into DNA storage, respectively. The fraction strategy contributes by delivering a cost-effective solution for image storage in DNA. The codec method offers an encryption mechanism to convert binary data into DNA bases by avoiding hybridization errors and satisfying the critical bio-coding constraints responsible for DNA storage durability. The robustness of RFS-codec is computed with GC and homopolymer constraints. Experimentally, different image data are efficiently encoded and decoded successfully with 1.8 bit/nt average density. RFS-codec's results demonstrate substantial advantages in constructing cost-effective, scalable, and durable DNA data storage.
Graphical Abstract
RFS-codec: A Novel Encoding Approach to Store Image Data in DNA
Keywords
DNA data storage
image fraction
codec approach
bio-coding constraints
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The author declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
References
  1. Church, G. M., Gao, Y., & Kosuri, S. (2012). Next-generation digital information storage in DNA. Science, 337(6102), 1628.
    [CrossRef]   [Google Scholar]
  2. Erlich, Y., & Zielinski, D. (2017). DNA Fountain enables a robust and efficient storage architecture. Science, 355(6328), 950-953.
    [CrossRef]   [Google Scholar]
  3. Cao, B., Wang, K., Xie, L., Zhang, J., Zhao, Y., Wang, B., & Zheng, P. (2024). PELMI: Realize robust DNA image storage under general errors via parity encoding and local mean iteration. Briefings in Bioinformatics, 25(5), bbae463.
    [CrossRef]   [Google Scholar]
  4. Song, L., Geng, F., Gong, Z. Y., Chen, X., Tang, J., Gong, C., ... & Yuan, Y. J. (2022). Robust data storage in DNA by de Bruijn graph-based de novo strand assembly. Nature communications, 13(1), 5361.
    [CrossRef]   [Google Scholar]
  5. Goldman, N., Bertone, P., Chen, S., Dessimoz, C., LeProust, E. M., Sipos, B., & Birney, E. (2013). Towards practical, high-capacity, low-maintenance information storage in synthesized DNA. nature, 494(7435), 77-80.
    [CrossRef]   [Google Scholar]
  6. Davis, J. (1996). Microvenus. Art Journal, 55(1), 70-74.
    [CrossRef]   [Google Scholar]
  7. Bancroft, C., Bowler, T., Bloom, B., & Clelland, C. T. (2001). Long-term storage of information in DNA. Science, 293(5536), 1763-1765.
    [CrossRef]   [Google Scholar]
  8. Pan, C., Tabatabaei, S. K., Tabatabaei Yazdi, S. M. H., Hernandez, A. G., Schroeder, C. M., & Milenkovic, O. (2022). Rewritable two-dimensional DNA-based data storage with machine learning reconstruction. Nature Communications, 13(1), 2984.
    [CrossRef]   [Google Scholar]
  9. Cao, B., Zheng, Y., Shao, Q., Liu, Z., Xie, L., Zhao, Y., ... & Wei, X. (2024). Efficient data reconstruction: The bottleneck of large-scale application of DNA storage. Cell Reports, 43(4).
    [CrossRef]   [Google Scholar]
  10. Antkowiak, P. L., Lietard, J., Darestani, M. Z., Somoza, M. M., Stark, W. J., Heckel, R., & Grass, R. N. (2020). Low cost DNA data storage using photolithographic synthesis and advanced information reconstruction and error correction. Nature communications, 11(1), 5345.
    [CrossRef]   [Google Scholar]
  11. Banal, J. L., Shepherd, T. R., Berleant, J., Huang, H., Reyes, M., Ackerman, C. M., ... & Bathe, M. (2021). Random access DNA memory using Boolean search in an archival file storage system. Nature materials, 20(9), 1272-1280.
    [CrossRef]   [Google Scholar]
  12. Zhou, S., Zhang, Q., & Wei, X. (2010). Image encryption algorithm based on DNA sequences for the big image. 2010 International Conference on Multimedia Information Networking and Security, 884-888.
    [CrossRef]   [Google Scholar]
  13. Fan, Q., Lilja, D. J., & Sapatnekar, S. S. (2020). Adaptive-length coding of image data for low-cost approximate storage. IEEE Transactions on Computers, 69(2), 239-252.
    [CrossRef]   [Google Scholar]
  14. Li, Q., Shi, L., Yang, J., Zhang, Y., & Xue, C. J. (2019). Leveraging approximate data for robust flash storage. 2019 56th ACM/IEEE Design Automation Conference (DAC), 1-6.
    [CrossRef]   [Google Scholar]
  15. Organick, L., Ang, S. D., Chen, Y. J., Lopez, R., Yekhanin, S., Makarychev, K., ... Strauss, K. (2018). Random access in large-scale DNA data storage. Nature Biotechnology, 36(3), 242-248.
    [CrossRef]   [Google Scholar]
  16. Cao, B., Zhang, X., Cui, S., & Zhang, Q. (2022). Adaptive coding for DNA storage with high storage density and low coverage. NPJ systems biology and applications, 8(1), 23.
    [CrossRef]   [Google Scholar]
Cite This Article
APA Style
Rasool, A. (2025). RFS-codec: A Novel Encoding Approach to Store Image Data in DNA. Journal of Artificial Intelligence in Bioinformatics, 1(1), 41–50. https://doi.org/10.62762/JAIB.2025.146324
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CC BY Copyright © 2025 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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