期刊
NATURE BIOTECHNOLOGY
卷 36, 期 3, 页码 239-+出版社
NATURE PORTFOLIO
DOI: 10.1038/nbt.4061
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资金
- National Research Foundation of Korea [2017R1A2B3004198, 2017M3A9B4062403, 2013M3A9B4076544, 2014M3C9A3063541]
- Brain Korea 21 Plus Project (Yonsei University College of Medicine)
- Brain Korea 21 Plus Project (SNU ECE)
- Institute for Basic Science (IBS) [IBS-R026-D1]
- Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea [HI17C0676, HI16C1012]
- National Research Foundation of Korea [2013M3A9B4076544, 2017R1A2B3004198, 2017M3A9B4062403] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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.
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