Effective use of sequence information to predict CRISPR-Cas9 off-target

Abstract

The CRISPR/Cas9 gene-editing system is the third-generation gene-editing technology that has been widely used in biomedical applications. However, off-target effects occurring CRISPR/Cas9 system has been a challenging problem it faces in practical applications. Although many predictive models have been developed to predict off-target activities, current models do not effectively use sequence pair information. There is still room for improved accuracy. This study aims to effectively use sequence pair information to improve the model's performance for predicting off-target activities. We propose a new coding scheme for coding sequence pairs and design a new model called CRISPR-IP for predicting off-target activity. Our coding scheme distinguishes regions with different functions in the sequence pairs through the function channel. Moreover, it distinguishes between bases and base pairs using type channels, effectively representing the sequence pair information. The CRISPR-IP model is based on CNN, BiLSTM, and the attention layer to learn features of sequence pairs. We performed performance verification on two data sets and found that our coding scheme can represent sequence pair information effectively, and the CRISPR-IP model performance is better than others. Data and source codes are available at https://github.com/BioinfoVirgo/CRISPR-IP.

Keywords: A, Adenine; BiLSTM, Bi-directional Long-Short Term Memory; C, Cytosine; CDF, Cutting frequency determination; CNN, Convolutional Neural Networks; CRISPR-Cas9; CRISPR-IP, CRISPR model based on Identity and Position; CRISPR/Cas9, Clustered Regularly Interspaced Short Palindromic Repeats / CRISPR associated protein 9; DNN, Dense Neural Networks; Deep learning; Encoding scheme; G, Guanine; GRU, Gate Recurrent Unit; LOGOCV, Leave-one-gRNA-out cross-validation; LSTM, Long-Short Term Memory; Off-target prediction; PAM, Protospacer adjacent motif; PR-AUC, Area Under the Precision-Recall Curve; RNN, Recurrent Neural Networks; ROC-AUC, Area Under the Receiver Operating Characteristic Curve; T, Thymine; U, Uracil; gRNA, Guide RNA.