Accurate cross-species 5mC detection for Oxford Nanopore sequencing in plants with DeepPlant

He-Xu Chen, Zhen-Dong Liu, Xin Bai, Bo Wu, Rong Song, Hui-Cong Yao, Ying Chen, Wei Chi (), Qian Hua (), Liang Cheng () and Chuan-Le Xiao ()
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He-Xu Chen: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science
Zhen-Dong Liu: Shanghai Polytechnic University
Xin Bai: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science
Bo Wu: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science
Rong Song: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science
Hui-Cong Yao: Sun Yat-Sen University
Ying Chen: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science
Wei Chi: Southern Medical University
Qian Hua: Beijing University of Chinese Medicine
Liang Cheng: Harbin Medical University
Chuan-Le Xiao: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract Nanopore sequencing enables comprehensive detection of 5-methylcytosine (5mC), particularly in repeat regions. However, CHH methylation detection in plants is limited by the scarcity of high-methylation positive samples, reducing generalization across species. Dorado, the only tool for plant 5mC detection on the R10.4 platform, lacks extensive species testing. Here, we develop DeepPlant, a deep learning model incorporating both Bi-LSTM and Transformer architectures, which significantly improves CHH detection accuracy and performs well for CpG and CHG motifs. We address the scarcity of methylation-positive CHH training samples through screening species with abundant high-methylation CHH sites using bisulfite-sequencing and generate datasets that cover diverse 9-mer motifs for training and testing DeepPlant. Evaluated across nine species, DeepPlant achieves high whole-genome methylation frequency correlations (0.705-0.838) with BS-seq data on CHH, improved by 23.4- 117.6% compared to Dorado. DeepPlant also demonstrates superior single-molecule accuracy and F1 score, offering strong generalization for plant epigenetics research.

Date: 2025
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DOI: 10.1038/s41467-025-58576-x

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