CRISPR-Net: A Recurrent Convolutional Network Quantifies CRISPR Off-Target Activities with Mismatches and Indels

Jiecong Lin

Abstract: The off-target effects induced by guide RNAs in the CRISPR/Cas9 gene-editing system have raised substantial concerns in recent years. Many in silico predictive models have been developed for predicting the off-target activities; however, few are capable of predicting the off-target activities with insertions or deletions between guide RNA and target DNA sequence pair. In order to fill this gap, a recurrent convolutional network named CRISPR-Net is developed for scoring the gRNA-target pairs with mismatches and indels; and a machine-learning based model named CRISPR-Net-Aggregate is also developed for aggregating the scores as the consensus off-target score for each potential guide RNA. It is demonstrated that CRISPR-Net achieves competitive performance on CIRCLE-Seq and GUIDE-seq datasets with indels and mismatches, outperforming the state-of-the-art off-target prediction methods on two independent mismatch-only datasets. The CRISPR-Net-Aggregate also surpasses a competing method on the aggregation task. Moreover, a two-stage sensitivity analysis is introduced to visualize the CRISPR-Net prediction on the gRNA-target pair of interest, demonstrating how implicit knowledge encoded in CRISPR-Net contributes to the accurate off-target activity quantification. Finally, the source code is made available at the Code Ocean repository (https://codeocean.com/capsule/9553651/tree/v1).