Evaluation of efficiency prediction algorithms and development of ensemble model for CRISPR/Cas9 gRNA selection

Abstract

Motivation: The CRISPR/Cas9 system is widely used for genome editing. The editing efficiency of CRISPR/Cas9 is mainly determined by the guide RNA (gRNA). Although many computational algorithms have been developed in recent years, it is still a challenge to select optimal bioinformatics tools for gRNA design in different experimental settings. Results: We performed a comprehensive comparison analysis of 15 public algorithms for gRNA design, using 16 experimental gRNA datasets. Based on this analysis, we identified the top-performing algorithms, with which we further implemented various computational strategies to build ensemble models for performance improvement. Validation analysis indicates that the new ensemble model had improved performance over any individual algorithm alone at predicting gRNA efficacy under various experimental conditions.