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Accurate detection of m6A RNA modifications in native RNA sequences

Author

Listed:
  • Huanle Liu

    (The Barcelona Institute of Science and Technology
    Garvan Institute of Medical Research)

  • Oguzhan Begik

    (The Barcelona Institute of Science and Technology
    Garvan Institute of Medical Research
    St-Vincent’s Clinical School, UNSW Sydney)

  • Morghan C. Lucas

    (The Barcelona Institute of Science and Technology
    Universitat Pompeu Fabra (UPF))

  • Jose Miguel Ramirez

    (The Barcelona Institute of Science and Technology)

  • Christopher E. Mason

    (Weill Cornell Medicine
    Weill Cornell Medicine
    Weill Cornell Medicine)

  • David Wiener

    (Weizmann Institute of Science)

  • Schraga Schwartz

    (Weizmann Institute of Science)

  • John S. Mattick

    (Garvan Institute of Medical Research
    St-Vincent’s Clinical School, UNSW Sydney
    Green templeton College)

  • Martin A. Smith

    (St-Vincent’s Clinical School, UNSW Sydney
    Garvan Institute of Medical Research)

  • Eva Maria Novoa

    (The Barcelona Institute of Science and Technology
    Garvan Institute of Medical Research
    St-Vincent’s Clinical School, UNSW Sydney
    Universitat Pompeu Fabra (UPF))

Abstract

The epitranscriptomics field has undergone an enormous expansion in the last few years; however, a major limitation is the lack of generic methods to map RNA modifications transcriptome-wide. Here, we show that using direct RNA sequencing, N6-methyladenosine (m6A) RNA modifications can be detected with high accuracy, in the form of systematic errors and decreased base-calling qualities. Specifically, we find that our algorithm, trained with m6A-modified and unmodified synthetic sequences, can predict m6A RNA modifications with ~90% accuracy. We then extend our findings to yeast data sets, finding that our method can identify m6A RNA modifications in vivo with an accuracy of 87%. Moreover, we further validate our method by showing that these ‘errors’ are typically not observed in yeast ime4-knockout strains, which lack m6A modifications. Our results open avenues to investigate the biological roles of RNA modifications in their native RNA context.

Suggested Citation

  • Huanle Liu & Oguzhan Begik & Morghan C. Lucas & Jose Miguel Ramirez & Christopher E. Mason & David Wiener & Schraga Schwartz & John S. Mattick & Martin A. Smith & Eva Maria Novoa, 2019. "Accurate detection of m6A RNA modifications in native RNA sequences," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11713-9
    DOI: 10.1038/s41467-019-11713-9
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    Cited by:

    1. Zhen-Dong Zhong & Ying-Yuan Xie & Hong-Xuan Chen & Ye-Lin Lan & Xue-Hong Liu & Jing-Yun Ji & Fu Wu & Lingmei Jin & Jiekai Chen & Daniel W. Mak & Zhang Zhang & Guan-Zheng Luo, 2023. "Systematic comparison of tools used for m6A mapping from nanopore direct RNA sequencing," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Jianheng Liu & Tao Huang & Jing Yao & Tianxuan Zhao & Yusen Zhang & Rui Zhang, 2023. "Epitranscriptomic subtyping, visualization, and denoising by global motif visualization," Nature Communications, Nature, vol. 14(1), pages 1-16, December.

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