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Atlas of quantitative single-base-resolution N6-methyl-adenine methylomes

Author

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  • Casslynn W. Q. Koh

    (Genome Institute of Singapore)

  • Yeek Teck Goh

    (Genome Institute of Singapore)

  • W. S. Sho Goh

    (Genome Institute of Singapore)

Abstract

Various methyltransferases and demethylases catalyse methylation and demethylation of N6-methyladenosine (m6A) and N6,2′-O-dimethyladenosine (m6Am) but precise methylomes uniquely mediated by each methyltransferase/demethylase are still lacking. Here, we develop m6A-Crosslinking-Exonuclease-sequencing (m6ACE-seq) to map transcriptome-wide m6A and m6Am at quantitative single-base-resolution. This allows for the generation of a comprehensive atlas of distinct methylomes uniquely mediated by every individual known methyltransferase or demethylase. Our atlas reveals METTL16 to indirectly impact manifold methylation targets beyond its consensus target motif and highlights the importance of precision in mapping PCIF1-dependent m6Am. Rather than reverse RNA methylation, we find that both ALKBH5 and FTO instead maintain their regulated sites in an unmethylated steady-state. In FTO’s absence, anomalous m6Am disrupts snRNA interaction with nuclear export machinery, potentially causing aberrant pre-mRNA splicing events.

Suggested Citation

  • Casslynn W. Q. Koh & Yeek Teck Goh & W. S. Sho Goh, 2019. "Atlas of quantitative single-base-resolution N6-methyl-adenine methylomes," Nature Communications, Nature, vol. 10(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-13561-z
    DOI: 10.1038/s41467-019-13561-z
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    Cited by:

    1. P Acera Mateos & A J Sethi & A Ravindran & A Srivastava & K Woodward & S Mahmud & M Kanchi & M Guarnacci & J Xu & Z W S Yuen & Y Zhou & A Sneddon & W Hamilton & J Gao & L M Starrs & R Hayashi & V Wick, 2024. "Prediction of m6A and m5C at single-molecule resolution reveals a transcriptome-wide co-occurrence of RNA modifications," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    2. Zhiyuan Luo & Jiacheng Zhang & Jingyi Fei & Shengdong Ke, 2022. "Deep learning modeling m6A deposition reveals the importance of downstream cis-element sequences," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    3. 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.
    4. Adrian Chan & Isabel S. Naarmann-de Vries & Carolin P. M. Scheitl & Claudia Höbartner & Christoph Dieterich, 2024. "Detecting m6A at single-molecular resolution via direct RNA sequencing and realistic training data," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    5. Ziyuan Wang & Yinshan Fang & Ziyang Liu & Ning Hao & Hao Helen Zhang & Xiaoxiao Sun & Jianwen Que & Hongxu Ding, 2024. "Adapting nanopore sequencing basecalling models for modification detection via incremental learning and anomaly detection," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    6. You Wu & Wenna Shao & Mengxiao Yan & Yuqin Wang & Pengfei Xu & Guoqiang Huang & Xiaofei Li & Brian D. Gregory & Jun Yang & Hongxia Wang & Xiang Yu, 2024. "Transfer learning enables identification of multiple types of RNA modifications using nanopore direct RNA sequencing," Nature Communications, Nature, vol. 15(1), pages 1-19, December.

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