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Adapting nanopore sequencing basecalling models for modification detection via incremental learning and anomaly detection

Author

Listed:
  • Ziyuan Wang

    (University of Arizona)

  • Yinshan Fang

    (Columbia University Medical Center)

  • Ziyang Liu

    (University of Arizona
    University of Arizona)

  • Ning Hao

    (University of Arizona
    University of Arizona)

  • Hao Helen Zhang

    (University of Arizona
    University of Arizona)

  • Xiaoxiao Sun

    (University of Arizona
    University of Arizona)

  • Jianwen Que

    (Columbia University Medical Center)

  • Hongxu Ding

    (University of Arizona
    University of Arizona)

Abstract

We leverage machine learning approaches to adapt nanopore sequencing basecallers for nucleotide modification detection. We first apply the incremental learning (IL) technique to improve the basecalling of modification-rich sequences, which are usually of high biological interest. With sequence backbones resolved, we further run anomaly detection (AD) on individual nucleotides to determine their modification status. By this means, our pipeline promises the single-molecule, single-nucleotide, and sequence context-free detection of modifications. We benchmark the pipeline using control oligos, further apply it in the basecalling of densely-modified yeast tRNAs and E.coli genomic DNAs, the cross-species detection of N6-methyladenosine (m6A) in mammalian mRNAs, and the simultaneous detection of N1-methyladenosine (m1A) and m6A in human mRNAs. Our IL-AD workflow is available at: https://github.com/wangziyuan66/IL-AD .

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51639-5
    DOI: 10.1038/s41467-024-51639-5
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    1. Alexander M. Price & Katharina E. Hayer & Alexa B. R. McIntyre & Nandan S. Gokhale & Jonathan S. Abebe & Ashley N. Fera & Christopher E. Mason & Stacy M. Horner & Angus C. Wilson & Daniel P. Depledge , 2020. "Direct RNA sequencing reveals m6A modifications on adenovirus RNA are necessary for efficient splicing," Nature Communications, Nature, vol. 11(1), pages 1-17, December.
    2. Qian Liu & Li Fang & Guoliang Yu & Depeng Wang & Chuan-Le Xiao & Kai Wang, 2019. "Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    3. Modi Safra & Aldema Sas-Chen & Ronit Nir & Roni Winkler & Aharon Nachshon & Dan Bar-Yaacov & Matthias Erlacher & Walter Rossmanith & Noam Stern-Ginossar & Schraga Schwartz, 2017. "The m1A landscape on cytosolic and mitochondrial mRNA at single-base resolution," Nature, Nature, vol. 551(7679), pages 251-255, November.
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