Anticipating On-Target and Off-Target Effects of CRISPR/Cas9 Genome Editing Via a Feedforward Neural Network Model

Abstract

Background

Clustered regularly interspaced short palindromic repeats —CRISPR-associated protein 9 (CRISPR/Cas9) is a gene editing technology that can deliver highly precise genome editing. However, it is difficult to predict both on- and off-target effects of CRISPR/Cas9, which is essential for ensuring the safety and efficiency of genetic modifications made using this technology.

Methods

In this study, we used the SITE-Seq dataset, which comprises CRISPR targets, to classify sequences for both on- and off-target effects. To evaluate sequence pairs, we built a feedforward neural network (FNN) with 10 fully connected layers and compared its performance with that of other state-of-the-art models.

Results

We showed that our FNN model attained an accuracy rate of 0.95, greatly improving prediction reliability for both on- and off-target effects compared with other methods.

Conclusion

This work contributes a valuable predictive modeling framework to the field of CRISPR research, addressing both on- and off-target effects in a unified manner, which is an essential requirement for the safe and effective application of genomic editing technologies.