Machine Learning-Driven Prediction of CRISPR-Cas9 Off-Target Effects and Mechanistic Insights

Abstract

The precise prediction of off-target effects in CRISPR-Cas9 genome editing is critical for ensuring the safety and efficacy of this powerful tool. This study leverages machine learning techniques to predict off-target cleavage sites and investigate the underlying mechanisms that affect cleavage efficiencies. By integrating data from Tsai et al. and Kleinsteiver et al., who employed the GUIDE-seq method, we aim to enhance our understanding of the factors influencing CRISPR-Cas9 activity.

Keywords

• crispr-cas9
• machine learning
• on-targets
• off-targets
• genome editing