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

Xiaolong Cheng, Zexu Li, Ruocheng Shan, Zihan Li, Shengnan Wang, Wenchang Zhao, Han Zhang, Lumen Chao, Jian Peng, Teng Fei () and Wei Li ()
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Xiaolong Cheng: Children’s National Hospital
Zexu Li: Northeastern University
Ruocheng Shan: Children’s National Hospital
Zihan Li: Northeastern University
Shengnan Wang: Northeastern University
Wenchang Zhao: Northeastern University
Han Zhang: Northeastern University
Lumen Chao: Children’s National Hospital
Jian Peng: University of Illinois at Urbana-Champaign
Teng Fei: Northeastern University
Wei Li: Children’s National Hospital

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: 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 .

Date: 2023
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DOI: 10.1038/s41467-023-36316-3

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