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

Abstract

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.