Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity

Abstract

Using deep learning to combine target sequence and chromatin accessibility data boosts the accuracy of CRISPR–Cpf1 guide RNA activity We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.