Abstract
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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Acknowledgements
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.)).
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Contributions
H.K.K., M.S., and S.J. performed experiments to build data sets of AsCpf1 indel frequencies. S.M. and S.Y. developed the framework, and carried out the model training and computational validation. J.W.C. performed bioinformatic analyses. Y.K. and S.L. made substantial contributions to the performance of the experiments including cell culture and deep-sequencing. H.H.K. conceived and designed the study. H.K.K., S.M., S.Y., and H.H.K. analyzed the data and wrote the manuscript.
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Yonsei University and Seoul National University have filed a patent based on this work, in which H.K.K., S.M., M.S., S.J., S.Y., and H.K. are co-inventors.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–14 and Supplementary Note (PDF 2816 kb)
Supplementary Tables
All tables that are included together, Supplementary tables 2, 4, and 6 (PDF 521 kb)
Supplementary Table 1
Source data used for this study. (XLSX 2463 kb)
Supplementary Table 3
Model selection results of Seq-deepCpf1 (XLSX 19 kb)
Supplementary Table 5
Oligonucleotides used in this study (XLSX 40 kb)
Supplementary Table 7
Confidence intervals for the result values (XLSX 15 kb)
Supplementary Code
The source code of Seq-deepCpf1 and DeepCpf1 (ZIP 750 kb)
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Kim, H., Min, S., Song, M. et al. Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity. Nat Biotechnol 36, 239–241 (2018). https://doi.org/10.1038/nbt.4061
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DOI: https://doi.org/10.1038/nbt.4061
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