PlantDeepMeth: A Deep Learning Model for Predicting DNA Methylation States in Plants

文献类型： 外文期刊

第一作者： Guo, Zhongwei

作者： Guo, Zhongwei;Hou, Xilin;Li, Ying;Guo, Zhongwei;Fan, Wenyuan;Cai, Chengcheng;Zhang, Kang;Cheng, Feng;Cheng, Feng

作者机构：

关键词： deep learning; convolutional neural networks; recurrent neural network; methylation state; plant epigenomics

期刊名称：PLANTS-BASEL （ 影响因子：4.1； 五年影响因子：4.5 ）

ISSN： 2223-7747

年卷期： 2025 年 14 卷 11 期

页码：

收录情况： SCI

摘要： 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 Brassica rapa and Arabidopsis thaliana 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 B. rapa and A. thaliana, further revealing key regulatory patterns captured by the model. Moreover, cross-species validation between B. rapa and A. thaliana 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.

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