CRISPR Off-Target Risk Prediction and Uncertainty

Abstract

Due to their high efficiency and programmability, CRISPR/Cas systems are commonly used in gene function experiments and gene therapy, but off-target effects are still a big problem-Cas proteins may cut non-target sites. Most machine learning and deep learning methods for predicting the off-target activity of gRNA–DNA pairs are point predictions, which are hard to quantify and weaken the validity of the assessment and amplify the risk of decision-making in high-risk scenarios. Therefore, uncertainty quantification approaches have been increasingly applied into the research of this field. This paper reviews the related works on applying uncertainty modeling in this field. Specifically, the concepts of data uncertainty and model uncertainty and their modeling techniques will be reviewed, including heteroscedastic regression, probabilistic predictive models, Deep Ensembles and MC Dropout. Furthermore, this paper will introduce some key metrics for the evaluation of the reliability of uncertainty models, including predictive accuracy, confidence interval calibration and performance on out-of-distribution (OOD) data. By reviewing the related studies, this paper concludes that it is very important to use uncertainty-aware models to improve the reliability of CRISPR predictions in this field.