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Related Concept Videos

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Updated: Jun 24, 2026

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Learning to quantify uncertainty in off-target activity for CRISPR guide RNAs.

Furkan Özden1, Peter Minary1

  • 1Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK.

Nucleic Acids Research
|September 14, 2024
PubMed
Summary

CRISPR genome editing faces challenges with off-target effects. crispAI predicts uncertainty in off-target activity and offers a genome-wide sgRNA efficiency score for better risk assessment in genetic manipulation.

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • CRISPR-based genome editing offers precise genetic manipulation but is limited by off-target effects.
  • Current prediction methods focus on point estimates, not fully capturing risks.
  • Uncertainty in off-target activity is crucial for clinical applications.

Purpose of the Study:

  • To develop a novel approach for predicting uncertainty estimates of CRISPR off-target cleavage activity.
  • To introduce crispAI-aggregate, a genome-wide single guide RNA (sgRNA) efficiency score.
  • To provide a more comprehensive risk assessment for CRISPR applications.

Main Methods:

  • Utilized a neural network architecture for predicting uncertainty.
  • Employed the Zero Inflated Negative Binomial (ZINB) model to capture count noise in off-target data.
  • Developed crispAI-aggregate for genome-wide sgRNA efficiency scoring.

Main Results:

  • crispAI provides calibrated uncertainty estimates for off-target cleavage.
  • The approach demonstrates superior predictive performance over existing methods.
  • crispAI-aggregate offers richer information for sgRNA prioritization.

Conclusions:

  • crispAI enhances risk assessment in CRISPR genome editing by quantifying prediction uncertainty.
  • The developed tool facilitates improved decision-making in sgRNA design.
  • This work advances the safety and applicability of CRISPR technologies.