C-RNNCrispr: Prediction of CRISPR/Cas9 sgRNA activity using convolutional and recurrent neural networks
Guishan Zhang, Zhiming Dai, Xianhua Dai
Abstract
CRISPR/Cas9 is a hot genomic editing tool, but its success is limited by the widely varying target efficiencies among different single guide RNAs (sgRNAs). In this study, we proposed C-RNNCrispr, a hybrid convolutional neural networks (CNNs) and bidirectional gate recurrent unit network (BGRU) framework, to predict CRISPR/Cas9 sgRNA on-target activity. C-RNNCrispr consists of two branches: sgRNA branch and epigenetic branch. The network receives the encoded binary matrix of sgRNA sequence and four epigenetic features as inputs, and produces a regression score. We introduced a transfer learning approach by using small-size datasets to fine-tune C-RNNCrispr model that were pre-trained from benchmark dataset, leading to substantially improved predictive performance. Experiments on commonly used datasets showed C-RNNCrispr outperforms the state-of-the-art methods in terms of prediction accuracy and generalization. Source codes are available at https://github.com/Peppags/C_RNNCrispr.