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

Xiaolong Cheng1,2, Zexu Li3, Ruocheng Shan1,4

  • 1Center for Genetic Medicine Research, Children's National Hospital, Washington, DC, 20010, USA.

Nature Communications
|February 10, 2023
PubMed

Insights

We developed DeepCas13, a deep learning model to predict CRISPR-Cas13d guide efficiency for RNA targeting. This tool improves accuracy in predicting on-target and off-target effects, aiding gene function studies.

Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • CRISPR-Cas13d systems offer powerful RNA manipulation tools.
  • Accurate prediction of guide RNA on-target and off-target effects remains a significant challenge.

Purpose of the Study:

  • To develop a deep learning model, DeepCas13, for predicting CRISPR-Cas13d on-target activity.
  • To enhance the prediction of guide efficiency for targeting both protein-coding and non-coding RNAs.

Main Methods:

  • CRISPR-Cas13d proliferation screens were conducted.
  • A deep learning model, DeepCas13, was designed using guide sequences and RNA secondary structures.
  • Model performance was validated using secondary screens and quantitative RT-PCR.

Main Results:

  • DeepCas13 demonstrated superior performance over existing methods in predicting guide efficiency.
  • Off-target viability effects were observed for guides targeting non-essential genes, correlating with on-target efficiency.
  • lncRNAs impacting cell viability and proliferation were identified using DeepCas13.

Conclusions:

  • DeepCas13 accurately predicts CRISPR-Cas13d guide activity, improving RNA targeting predictions.
  • Proper normalization strategies using negative controls are crucial for mitigating false positives in proliferation screens.
  • The study identifies novel lncRNAs involved in cell viability and proliferation.

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