Prediction of activity and specificity of CRISPR-Cpf1 using convolutional deep learning neural networks

Abstract

Background

CRISPR-Cpf1 has recently been reported as another RNA-guided endonuclease of class 2 CRISPR-Cas system, which expands the molecular biology toolkit for genome editing. However, most of the online tools and applications to date have been developed primarily for the Cas9. There are a limited number of tools available for the Cpf1.

Results

We present DeepCpf1, a deep convolution neural networks (CNN) approach to predict Cpf1 guide RNAs on-target activity and off-target effects using their matched and mismatched DNA sequences. Trained on published data sets, DeepCpf1 is superior to other machine learning algorithms and reliably predicts the most efficient and less off-target effects guide RNAs for a given gene. Combined with a permutation importance analysis, the key features of guide RNA sequences are identified, which determine the activity and specificity of genome editing.

Conclusions

DeepCpf1 can significantly improve the accuracy of Cpf1-based genome editing and facilitates the generation of optimized guide RNAs libraries.