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

Xiaolong Cheng, Center for Genetic Medicine Research, Children's National Hospital, Washington, DC, 20010, USA.
Zexu Li, National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang, 110819, China.
Ruocheng Shan, Center for Genetic Medicine Research, Children's National Hospital, Washington, DC, 20010, USA.
Zihan Li, National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang, 110819, China.
Shengnan Wang, National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang, 110819, China.
Wenchang Zhao, National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang, 110819, China.
Han Zhang, National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang, 110819, China.
Lumen Chao, Center for Genetic Medicine Research, Children's National Hospital, Washington, DC, 20010, USA.
Jian Peng, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
Teng Fei, National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang, 110819, China. feiteng@mail.neu.edu.cn.
Wei Li, Center for Genetic Medicine Research, Children's National Hospital, Washington, DC, 20010, USA. wli2@childrensnational.org.

Document Type

Journal Article

Publication Date

2-10-2023

Journal

Nature communications

Volume

14

Issue

1

DOI

10.1038/s41467-023-36316-3

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 .

Department

Genomics and Precision Medicine

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