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

Xiaolong Cheng, Zexu Li, Ruocheng Shan, Zihan Li, Lumen Chao, Jian Peng, Teng Fei, View ORCID ProfileWei Li
doi: https://doi.org/10.1101/2021.09.02.458773
Xiaolong Cheng
1Center for Genetic Medicine Research, Children’s National Hospital. Washington, DC, USA 20010
2Department of Genomics and Precision Medicine, George Washington University, Washington, DC, USA 20010
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Zexu Li
3College of Life and Health Sciences, Northeastern University, Shenyang, China
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Ruocheng Shan
1Center for Genetic Medicine Research, Children’s National Hospital. Washington, DC, USA 20010
4Department of Computer Science, George Washington University, Washington, DC, USA
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Zihan Li
3College of Life and Health Sciences, Northeastern University, Shenyang, China
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Lumen Chao
1Center for Genetic Medicine Research, Children’s National Hospital. Washington, DC, USA 20010
2Department of Genomics and Precision Medicine, George Washington University, Washington, DC, USA 20010
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Jian Peng
5Department of Computer Science, University of Illinois at Urbana-Champaign. Urbana, IL 61801
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Teng Fei
3College of Life and Health Sciences, Northeastern University, Shenyang, China
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  • For correspondence: wli2@childrensnational.org feiteng@mail.neu.edu.cn
Wei Li
1Center for Genetic Medicine Research, Children’s National Hospital. Washington, DC, USA 20010
2Department of Genomics and Precision Medicine, George Washington University, Washington, DC, USA 20010
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  • ORCID record for Wei Li
  • For correspondence: wli2@childrensnational.org feiteng@mail.neu.edu.cn
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Abstract

A major challenge in the application of the CRISPR-Cas13d (RfxCas13d, or CasRx) RNA editing system is to accurately predict its guide RNA (gRNA) dependent on-target and off-target effect. Here, we performed CRISPR-Cas13d proliferation screens that target protein-coding genes and long non-coding RNAs (lncRNAs), followed by a systematic modeling of Cas13d on-target efficiency and off-target viability effect. We first designed a deep learning model, named DeepCas13, to predict the on-target activity of a gRNA with high accuracy from its sequence and secondary structure. DeepCas13 outperforms existing methods and accurately predicts the efficiency of guides targeting both protein-coding and non-coding RNAs (e.g., circRNAs and lncRNAs). Next, we systematically studied guides targeting non-essential genes, and found that the off-target viability effect, defined as the unintended effect of guides on cell viability, is closely related to their on-target RNA cleavage efficiency. This finding suggests that these gRNAs should be used as negative controls in proliferation screens to reduce false positives, possibly coming from the unwanted off-target viability effect of efficient guides. Finally, we applied these models to our screens that included guides targeting 234 lncRNAs, and identified lncRNAs that affect cell viability and proliferation in multiple cell lines. DeepCas13 is freely accessible via http://deepcas13.weililab.org.

Competing Interest Statement

WL is a paid consultant to Tavros Therapeutics, Inc. Others declared no competing interests.

Footnotes

  • http://deepcas13.weililab.org

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 04, 2021.
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Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches
Xiaolong Cheng, Zexu Li, Ruocheng Shan, Zihan Li, Lumen Chao, Jian Peng, Teng Fei, Wei Li
bioRxiv 2021.09.02.458773; doi: https://doi.org/10.1101/2021.09.02.458773
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Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches
Xiaolong Cheng, Zexu Li, Ruocheng Shan, Zihan Li, Lumen Chao, Jian Peng, Teng Fei, Wei Li
bioRxiv 2021.09.02.458773; doi: https://doi.org/10.1101/2021.09.02.458773

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