Computational tools for CRISPR Off-target detection: An overview

Abstract

The detection of off-targets is crucial for the application of CRISPR technology in both therapeutics and plant genome editing. Off-target effects can lead to unintended genome modifications, potentially disrupting gene function and causing adverse genomic consequences. These off-target regions may feature mismatches, insertions, or deletions and can vary based on their genomic location. Methods for detecting off-targets are generally classified into biased and unbiased categories. Unbiased methods include both in vitro and in vivo techniques, though biased methods are often preferred due to their time and cost efficiency. Biased methods typically involve in silico screening of off-targets, followed by validation through PCR or sequencing, supported by various bioinformatics tools. The advent of artificial intelligence has significantly impacted the CRISPR field. Machine learning and deep learning models, developed using experimental data from unbiased methods, have enhanced the identification of true off-targets. These AI-driven approaches not only improve accuracy but also facilitate the prediction of off-target effects in a more efficient manner. Furthermore, the integration of AI with CRISPR technology holds promise for optimizing guide RNA design, minimizing off-target activity, and tailoring CRISPR systems for specific applications. The combination of CRISPR and AI is expected to advance precision genome editing, paving the way for more reliable therapeutic interventions and agricultural innovations.