TransCrispr: Transformer Based Hybrid Model for Predicting CRISPR/Cas9 Single Guide RNA Cleavage Efficiency

CRISPR/Cas9 is a widely used genome editing tool for site-directed modification of deoxyribonucleic acid (DNA) nucleotide sequences. However, how to accurately predict and evaluate the on- and off-target effects of single guide RNA (sgRNA) is one of the key problems for CRISPR/Cas9 system. Using computational methods to obtain high cell-specific sensitivity and specificity is a prerequisite for the optimal design of sgRNAs. Inspired by the work of predecessors, we found that sgRNA on-target knockout efficacy was not only related to the original sequence but also affected by important biological features. Hence, we introduce a novel approach called TransCrispr, which integrates Transformer and convolutional neural network (CNN) architecture to predict sgRNA knockout efficacy. Firstly, we encode the sequence data and send the transformed sgRNA sequence, positional information, and biological features into the network as input. Then, the convolutional neural network will automatically learn an appropriate feature representation for the sgRNA sequence and combine it with the positional information for self-attention learning of the Transformer. Finally, a regression score is generated by predicting biological features. Experiments on seven public datasets illustrate that TransCrispr outperforms state-of-the-art methods in terms of prediction accuracy and generalization ability.