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Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches

Author

Listed:
  • Xiaolong Cheng

    (Children’s National Hospital
    George Washington University)

  • Zexu Li

    (Northeastern University)

  • Ruocheng Shan

    (Children’s National Hospital
    George Washington University)

  • Zihan Li

    (Northeastern University)

  • Shengnan Wang

    (Northeastern University)

  • Wenchang Zhao

    (Northeastern University)

  • Han Zhang

    (Northeastern University)

  • Lumen Chao

    (Children’s National Hospital
    George Washington University)

  • Jian Peng

    (University of Illinois at Urbana-Champaign)

  • Teng Fei

    (Northeastern University)

  • Wei Li

    (Children’s National Hospital
    George Washington University)

Abstract

A major challenge in the application of the CRISPR-Cas13d system is to accurately predict its guide-dependent on-target and off-target effect. Here, we perform CRISPR-Cas13d proliferation screens and design a deep learning model, named DeepCas13, to predict the on-target activity from guide sequences and secondary structures. DeepCas13 outperforms existing methods to predict the efficiency of guides targeting both protein-coding and non-coding RNAs. Guides targeting non-essential genes display off-target viability effects, which are closely related to their on-target efficiencies. Choosing proper negative control guides during normalization mitigates the associated false positives in proliferation screens. We apply DeepCas13 to the guides targeting lncRNAs, and identify lncRNAs that affect cell viability and proliferation in multiple cell lines. The higher prediction accuracy of DeepCas13 over existing methods is extensively confirmed via a secondary CRISPR-Cas13d screen and quantitative RT-PCR experiments. DeepCas13 is freely accessible via http://deepcas13.weililab.org .

Suggested Citation

  • Xiaolong Cheng & Zexu Li & Ruocheng Shan & Zihan Li & Shengnan Wang & Wenchang Zhao & Han Zhang & Lumen Chao & Jian Peng & Teng Fei & Wei Li, 2023. "Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36316-3
    DOI: 10.1038/s41467-023-36316-3
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    Cited by:

    1. Giho Kim & Ho Joon Kim & Keonwoo Kim & Hyeon Jin Kim & Jina Yang & Sang Woo Seo, 2024. "Tunable translation-level CRISPR interference by dCas13 and engineered gRNA in bacteria," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Yocelyn Recinos & Dmytro Ustianenko & Yow-Tyng Yeh & Xiaojian Wang & Martin Jacko & Lekha V. Yesantharao & Qiyang Wu & Chaolin Zhang, 2024. "CRISPR-dCas13d-based deep screening of proximal and distal splicing-regulatory elements," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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