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Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data

Author

Listed:
  • Qian Liu

    (Children’s Hospital of Philadelphia)

  • Li Fang

    (Children’s Hospital of Philadelphia)

  • Guoliang Yu

    (Sun Yat-sen University
    Grandomics Biosciences)

  • Depeng Wang

    (Grandomics Biosciences)

  • Chuan-Le Xiao

    (Sun Yat-sen University)

  • Kai Wang

    (Children’s Hospital of Philadelphia
    University of Pennsylvania)

Abstract

DNA base modifications, such as C5-methylcytosine (5mC) and N6-methyldeoxyadenosine (6mA), are important types of epigenetic regulations. Short-read bisulfite sequencing and long-read PacBio sequencing have inherent limitations to detect DNA modifications. Here, using raw electric signals of Oxford Nanopore long-read sequencing data, we design DeepMod, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) to detect DNA modifications. We sequence a human genome HX1 and a Chlamydomonas reinhardtii genome using Nanopore sequencing, and then evaluate DeepMod on three types of genomes (Escherichia coli, Chlamydomonas reinhardtii and human genomes). For 5mC detection, DeepMod achieves average precision up to 0.99 for both synthetically introduced and naturally occurring modifications. For 6mA detection, DeepMod achieves ~0.9 average precision on Escherichia coli data, and have improved performance than existing methods on Chlamydomonas reinhardtii data. In conclusion, DeepMod performs well for genome-scale detection of DNA modifications and will facilitate epigenetic analysis on diverse species.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10168-2
    DOI: 10.1038/s41467-019-10168-2
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    Cited by:

    1. Eseosa Halima Ighile & Hiroaki Shirakawa & Hiroki Tanikawa, 2022. "Application of GIS and Machine Learning to Predict Flood Areas in Nigeria," Sustainability, MDPI, vol. 14(9), pages 1-33, April.
    2. Dominik Stanojević & Zhe Li & Sara Bakić & Roger Foo & Mile Šikić, 2024. "Rockfish: A transformer-based model for accurate 5-methylcytosine prediction from nanopore sequencing," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    3. 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.
    4. Abdur Rasool & Qiang Qu & Yang Wang & Qingshan Jiang, 2022. "Bio-Constrained Codes with Neural Network for Density-Based DNA Data Storage," Mathematics, MDPI, vol. 10(5), pages 1-21, March.
    5. Mian Umair Ahsan & Anagha Gouru & Joe Chan & Wanding Zhou & Kai Wang, 2024. "A signal processing and deep learning framework for methylation detection using Oxford Nanopore sequencing," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    6. Gaolian Xu & Hao Yang & Jiani Qiu & Julien Reboud & Linqing Zhen & Wei Ren & Hong Xu & Jonathan M. Cooper & Hongchen Gu, 2023. "Sequence terminus dependent PCR for site-specific mutation and modification detection," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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