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

Peng Ni, Neng Huang, Fan Nie, Jun Zhang, Zhi Zhang, Bo Wu, Lu Bai, Wende Liu, Chuan-Le Xiao (), Feng Luo () and Jianxin Wang ()
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Peng Ni: Central South University
Neng Huang: Central South University
Fan Nie: Central South University
Jun Zhang: Central South University
Zhi Zhang: Central South University
Bo Wu: Clemson University
Lu Bai: Chinese Academy of Agricultural Sciences
Wende Liu: Chinese Academy of Agricultural Sciences
Chuan-Le Xiao: Sun Yat-sen University
Feng Luo: Clemson University
Jianxin Wang: Central South University

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-26278-9

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