A Novel Hybrid CNN-SVR for CRISPR/Cas9 Guide RNA Activity Prediction

Guishan Zhang; Zhiming Dai; Xianhua Dai

doi:10.3389/fgene.2019.01303

A Novel Hybrid CNN-SVR for CRISPR/Cas9 Guide RNA Activity Prediction

Front Genet. 2020 Jan 8:10:1303. doi: 10.3389/fgene.2019.01303. eCollection 2019.

Authors

Guishan Zhang¹, Zhiming Dai^{2

3}, Xianhua Dai^{1

4}

Affiliations

¹ School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China.
² School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China.
³ Guangdong Province Key Laboratory of Big Data Analysis and Processing, Sun Yat-sen University, Guangzhou, China.
⁴ Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, China.

Abstract

Accurate prediction of guide RNA (gRNA) on-target efficacy is critical for effective application of CRISPR/Cas9 system. Although some machine learning-based and convolutional neural network (CNN)-based methods have been proposed, prediction accuracy remains to be improved. Here, firstly we improved architectures of current CNNs for predicting gRNA on-target efficacy. Secondly, we proposed a novel hybrid system which combines our improved CNN with support vector regression (SVR). This CNN-SVR system is composed of two major components: a merged CNN as the front-end for extracting gRNA feature and an SVR as the back-end for regression and predicting gRNA cleavage efficiency. We demonstrate that CNN-SVR can effectively exploit features interactions from feed-forward directions to learn deeper features of gRNAs and their corresponding epigenetic features. Experiments on commonly used datasets show that our CNN-SVR system outperforms available state-of-the-art methods in terms of prediction accuracy, generalization, and robustness. Source codes are available at https://github.com/Peppags/CNN-SVR.

Keywords: CRISPR/Cas9; convolutional neural network; guide RNA; on-target; support vector regression.