CRISPR-OTE: Prediction of CRISPR On-Target Efficiency Based on Multi-Dimensional Feature Fusion

Abstract

Objective

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) is a powerful genome editing technology. Guide RNA (gRNA) plays an essential guiding role in the CRISPR system by complementary base pairing with target DNA. Since the CRISPR targeting mechanism problem has not yet been fully resolved, it remains a challenge to predict gRNA on-target efficiency. Current gRNA design tools often lack efficient information extraction and cannot learn the target efficiency patterns thoroughly.

Material and methods

In this study, CRISPR-OTE is proposed to consider both multi-dimensional sequence information and important complementary prior knowledge based on a simple but effective framework. CRISPR-OTE consists of the local-contextual information branch and the prior knowledge branch. The local-contextual information branch extracts multi-dimensional sequence features from the DNA primary sequence by a parallel framework of Convolutional Neural Networks (CNN) and bidirectional Long Short-Term Memory networks (biLSTM). The prior knowledge branch selects the optimal subset of physicochemical features to provide the neural network with complementary knowledge, such as complex secondary structures. A simple feature fusion strategy is also adopted to fully utilize multi-modal data from the two branches.

Results

The experimental results show that the optimal subset of physicochemical features (RNA secondary structure and melting temperature of 34nt target) can effectively improve the prediction performance. Additionally, combining multi-dimensional sequence features and multi-modal features can extract information more comprehensively. Through transfer learning, CRISPR-OTE trained on the CRISPR-Cpf1 system can also be successfully applied to the CRISPR-Cas9 system.

Conclusion

The performance of CRISPR-OTE is superior to other methods in different CRISPR systems and species. Therefore, CRISPR-OTE is a simple on-target efficiency prediction framework with better accuracy and generalization performance.