Examining Machine Learning Models That Predict sgRNA Cleavage Efficiencies

Abstract

Ever since its discovery, CRISPR-Cas9 has taken over the world in gene editing. By providing a single guide RNA to the cas9 enzyme, CRISPR-Cas9 can immediately pinpoint the target gene location in the genome and slice it. Scientists discovered a revolutionary way to use this method for gene editing. Yet, the challenge is that the CRISPR-Cas9 system is lenient with the matching precision of the guide RNA to the target sequence As a result, the CRISPR-cas9 system may also cleave certain healthy sequences that are almost identical to the target sequence. This paper aims to find the best model that uses machine learning to predict an optimal sgRNA design.