DL-CRISPR: A Deep Learning Method for Off-Target Activity Prediction in CRISPR/Cas9 With Data Augmentation

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR- associated (Cas) system is a popular and easy to use gene-editing technique, but it has off-target risk. Cutting the off-target sites will harm the cells severely, hence in silico methods are needed to help to avoid this. Most existing in silico approaches mainly relied on a relatively small positive dataset and the data imbalance issue still exists. Besides, some samples used to be considered as negative are later proved to be positive. Hence, it is essential to refresh the dataset and develop more accurate off-target activity prediction programs. In this work, firstly, we extended the current positive dataset and explored the potential differences between positive and negative data based on the new dataset. Then we adopted a new data augmentation method to solve the data imbalance issue, and used the ensemble idea to take more negative data into consideration to make the model close to the real scenario, but at the same time keeping the model balance. Finally, we developed DL-CRISPR, a deep learning framework to predict off-target activity in CRISPR/Cas9. DL-CRISPR is evaluated and compared with other state-of-the-art methods on three kinds of datasets: 5-fold cross validation test datasets, putative off-targets datasets related to specific single guide RNAs (sgRNAs), and putative off-targets datasets related to unseen sgRNAs. DL-CRISPR realizes the best average accuracy, i.e. 98.57%, on 5-fold cross validation datasets and correctly detects more off-targets on datasets related to both seen and unseen sgRNAs.