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.
Sequence Read Archive
The authors thank E.-S. Lee for proofreading and R. Gopalappa, N. Kim, S. Park, and J. Park for assisting in sample preparation. This work was supported in part by the National Research Foundation of Korea (grants 2017R1A2B3004198 (H.K.), 2017M3A9B4062403 (H.K.), 2013M3A9B4076544 (H.K.), 2014M3C9A3063541 (S.Y.)), Brain Korea 21 Plus Project (Yonsei University College of Medicine), Brain Korea 21 Plus Project (SNU ECE) in 2017, Institute for Basic Science (IBS; IBS-R026-D1), and the Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (grants HI17C0676 (H.K.), and HI16C1012 (H.K.)).
The source code of Seq-deepCpf1 and DeepCpf1