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

Author

Listed:
  • Peng Ni

    (Central South University
    Central South University)

  • Neng Huang

    (Central South University
    Central South University)

  • Fan Nie

    (Central South University
    Central South University)

  • Jun Zhang

    (Central South University
    Central South University)

  • Zhi Zhang

    (Central South University
    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
    Central South University)

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.

Suggested Citation

  • Peng Ni & Neng Huang & Fan Nie & Jun Zhang & Zhi Zhang & Bo Wu & Lu Bai & Wende Liu & Chuan-Le Xiao & Feng Luo & Jianxin Wang, 2021. "Genome-wide detection of cytosine methylations in plant from Nanopore data using deep learning," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26278-9
    DOI: 10.1038/s41467-021-26278-9
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    References listed on IDEAS

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    1. Zaka Wing-Sze Yuen & Akanksha Srivastava & Runa Daniel & Dennis McNevin & Cameron Jack & Eduardo Eyras, 2021. "Systematic benchmarking of tools for CpG methylation detection from nanopore sequencing," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    2. Zhengming Wang & David C. Baulcombe, 2020. "Transposon age and non-CG methylation," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    3. Ian R. Henderson & Steven E. Jacobsen, 2007. "Epigenetic inheritance in plants," Nature, Nature, vol. 447(7143), pages 418-424, May.
    4. Shawn J. Cokus & Suhua Feng & Xiaoyu Zhang & Zugen Chen & Barry Merriman & Christian D. Haudenschild & Sriharsa Pradhan & Stanley F. Nelson & Matteo Pellegrini & Steven E. Jacobsen, 2008. "Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning," Nature, Nature, vol. 452(7184), pages 215-219, March.
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    1. Tuomas Hämälä & Christopher Moore & Laura Cowan & Matthew Carlile & David Gopaulchan & Marie K. Brandrud & Siri Birkeland & Matthew Loose & Filip Kolář & Marcus A. Koch & Levi Yant, 2024. "Impact of whole-genome duplications on structural variant evolution in Cochlearia," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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