Prediction of off-target effects of the CRISPR/Cas9 system for design of sgRNA

Abstract

CRISPR/Cas9 genome editing technology is the frontier of life science research. They have been used to cure human genetic diseases, achieve cell personalized treatment, develop new drugs, and improve the genetic characteristics of crops and other fields. This system relies on the enzyme Cas9 cutting target DNA (on target) under the guidance of sgRNA, but it can also cut non-target sites, which results in offtarget effects, thus causing uncontrollable mutations. The risk of off-target effect in CRISPR technology is the main limiting factor that affects the widespread application of CRISPR technology. How to evaluate and reduce the off-target effect is the urgent problem to be solved. In this work, we build up a model that can predict the score of being off-target. Through comparison with the complete genome of the target and precise mathematics that calculate the potential risk of being off-target, we optimize the sgRNA, which is capable of reducing the off-target effect. The result has proven that we can efficiently and quickly identify and screen the best editing target sites with our model. The CRISPR/Cas9 system, not even being perfected yet, has already demonstrated its potential in the field of genome editing. Hopefully through our model, the CRISPR/Cas9 system can quickly apply to more branches in life science and cure those diseases that have been previously incurable.