文献类型： 外文期刊
作者： Guo, Zhongwei 1 ; Fan, Wenyuan 2 ; Cai, Chengcheng 2 ; Zhang, Kang 2 ; Hou, Xilin 1 ; Li, Ying 1 ; Cheng, Feng 2 ;
作者机构： 1.Nanjing Agr Univ, Coll Hort, Natl Key Lab Crop Genet & Germplasm Enhancement, Nanjing 210095, Peoples R China
2.Chinese Acad Agr Sci, Inst Vegetables & Flowers, State Key Lab Vegetable Biobreeding, Minist Agr & Rural Affairs,Key Lab Biol & Genet Im, Beijing 100081, Peoples R China
3.Key Lab Vegetable Biol Hainan Prov, Haikou 571100, Peoples R China
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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