Guo Zhongwei, Fan Wenyuan, Cai Chengcheng, Zhang Kang, Hou Xilin, Li Ying, Cheng Feng
National Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.
State Key Laboratory of Vegetable Biobreeding, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops of the Ministry of Agriculture and Rural Affairs, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Plants (Basel). 2025 Jun 5;14(11):1724. doi: 10.3390/plants14111724.
Cytosine DNA methylation (5mCs) is an important epigenetic modification in genomic research. However, the methylation states of some cytosine sites are not available due to the limitations of different studies, and there are few tools developed to deal with this problem, especially in plants, which have more methylation types than animals. Here, we report PlantDeepMeth, a novel deep learning model that utilizes deep learning to predict DNA methylation states in plants. The evaluation of PlantDeepMeth on known cytosine sites in both the and genomes shows good performance in predicting methylation states, indicating that the tool is good at learning patterns for methylation imputation. Motif analysis of the model's predictions identified specific motifs associated with hypo- or hyper-methylation states in and , further revealing key regulatory patterns captured by the model. Moreover, cross-species validation between and demonstrated the generalizability of PlantDeepMeth, with the model maintaining high performance across different plant species. These results highlight the effectiveness of PlantDeepMeth and demonstrate the potential of deep learning to advance plant genomics research.
胞嘧啶DNA甲基化（5mCs）是基因组研究中的一种重要表观遗传修饰。然而，由于不同研究的局限性，一些胞嘧啶位点的甲基化状态无法获得，并且针对这一问题开发的工具很少，尤其是在植物中，植物的甲基化类型比动物更多。在此，我们报告了PlantDeepMeth，这是一种利用深度学习来预测植物DNA甲基化状态的新型深度学习模型。PlantDeepMeth对拟南芥和水稻基因组中已知胞嘧啶位点的评估表明，其在预测甲基化状态方面表现良好，这表明该工具擅长学习甲基化插补模式。对该模型预测结果的基序分析确定了与拟南芥和水稻中低甲基化或高甲基化状态相关的特定基序，进一步揭示了该模型捕捉到的关键调控模式。此外，拟南芥和水稻之间的跨物种验证证明了PlantDeepMeth的通用性，该模型在不同植物物种中均保持高性能。这些结果突出了PlantDeepMeth的有效性，并证明了深度学习在推进植物基因组学研究方面的潜力。
