De novo non-canonical nanopore basecalling enables private communication using heavily-modified DNA data at single-molecule level

Qingyuan Fan, Xuyang Zhao, Junyao Li, Ronghui Liu, Ming Liu, Qishun Feng, Yanping Long, Yang Fu, Jixian Zhai, Qing Pan and Yi Li ()
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Qingyuan Fan: Southern University of Science and Technology
Xuyang Zhao: Southern University of Science and Technology
Junyao Li: Southern University of Science and Technology
Ronghui Liu: Southern University of Science and Technology
Ming Liu: Southern University of Science and Technology
Qishun Feng: The Second Affiliated Hospital of Southern University of Science and Technology
Yanping Long: Southern University of Science and Technology
Yang Fu: Southern University of Science and Technology
Jixian Zhai: Southern University of Science and Technology
Qing Pan: Zhejiang University of Technology
Yi Li: Southern University of Science and Technology

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract Hidden messages in DNA molecules by employing chemical modifications has been suggested for private data storage and transmission at high information density. However, rapidly decoding these “molecular keys” with corresponding basecallers remains challenging. We present DeepSME, a nanopore sequencing and deep-learning based framework towards single-molecule encryption, demonstrated by using 5-hydroxymethylcytosine (5hmC) substitution for individual nucleotide recognition rather than sequential interactions. This non-natural, motif-insensitive methylation disrupts ion current, resulting in a readout failure of 67.2%–100%, concealing the privacy within the DNAs. We further develop an alignment-free DeepSME basecaller as a key to reconstitute the digital information. Our three-stage training pipeline, expands k-mer size from 46 to 49, achieving over 92% precision and recall from scratch. DeepSME deciphers fully 5hmC concealed text and image within 16× coverage depth with an F1-score of 86.4%, surpassing all the state-of-the-art basecallers. Demonstrated on edge computing devices, DeepSME holds supreme potential for DNA-based private communications and broader bioengineering and medical applications.

Date: 2025
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DOI: 10.1038/s41467-025-59357-2

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