Hui Kwon Kim^1,2,9, Seonwoo Min^3,9, Myungjae Song^1,4, Soobin Jung^1,2, Jae Woo Choi^1,5, Younggwang Kim^1,2, Sangeun Lee^1,2, Sungroh Yoon^3,6,* & Hyongbum (Henry) Kim<sup>1,2,5,7,8,*

1</sup>Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea. ²Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea. ³Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea. ⁴Graduate School of Biomedical Science and Engineering, Hanyang University, Seoul, Republic of Korea. ⁵Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea. ⁶Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea. ⁷Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea. ⁸Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea. ⁹These authors contributed equally to this work.

^*Correspondence should be addressed to H.K. or S.Y..

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.