Genome-wide detection of cytosine methylations in plant from Nanopore data using deep learning

文献类型： 外文期刊

第一作者： Ni, Peng

作者： Ni, Peng;Huang, Neng;Nie, Fan;Zhang, Jun;Zhang, Zhi;Wang, Jianxin;Ni, Peng;Huang, Neng;Nie, Fan;Zhang, Jun;Zhang, Zhi;Wang, Jianxin;Wu, Bo;Luo, Feng;Bai, Lu;Liu, Wende;Xiao, Chuan-Le

作者机构：

期刊名称：NATURE COMMUNICATIONS （ 影响因子：14.919； 五年影响因子：15.805 ）

ISSN：

年卷期： 2021 年 12 卷 1 期

页码：

收录情况： SCI

摘要： In plants, cytosine DNA methylations (5mCs) can happen in three sequence contexts as CpG, CHG, and CHH (where H = A, C, or T), which play different roles in the regulation of biological processes. Although long Nanopore reads are advantageous in the detection of 5mCs comparing to short-read bisulfite sequencing, existing methods can only detect 5mCs in the CpG context, which limits their application in plants. Here, we develop DeepSignal-plant, a deep learning tool to detect genome-wide 5mCs of all three contexts in plants from Nanopore reads. We sequence Arabidopsis thaliana and Oryza sativa using both Nanopore and bisulfite sequencing. We develop a denoising process for training models, which enables DeepSignal-plant to achieve high correlations with bisulfite sequencing for 5mC detection in all three contexts. Furthermore, DeepSignal-plant can profile more 5mC sites, which will help to provide a more complete understanding of epigenetic mechanisms of different biological processes. Existing methods cannot profile genome-wide cytosine DNA methylations (5mCs) in all three contexts with acceptable accuracy. Here, the authors develop a deep learning tool to detect genome-wide 5mCs of all three contexts in plants with high accuracy from Nanopore reads.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序