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Nature Biotechnology
  • Brief Communication
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Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity

Nature Biotechnologyvolume 36, pages239–241 (2018)Cite this article

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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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Figure 1: Deep learning outperforms conventional machine learning for the task of predicting Cpf1 activity based on the target sequence composition.
Figure 2: Consideration of chromatin accessibility significantly improves the prediction of Cpf1 activities at endogenous target sites.

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Sequence Read Archive

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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.)).

Author information

Author notes

  1. Hui Kwon Kim and Seonwoo Min: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hui Kwon Kim, Myungjae Song, Soobin Jung, Jae Woo Choi, Younggwang Kim, Sangeun Lee & Hyongbum (Henry) Kim

  2. Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hui Kwon Kim, Soobin Jung, Younggwang Kim, Sangeun Lee & Hyongbum (Henry) Kim

  3. Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea

    Seonwoo Min & Sungroh Yoon

  4. Graduate School of Biomedical Science and Engineering, Hanyang University, Seoul, Republic of Korea

    Myungjae Song

  5. Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea

    Jae Woo Choi & Hyongbum (Henry) Kim

  6. Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea

    Sungroh Yoon

  7. Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea

    Hyongbum (Henry) Kim

  8. Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea

    Hyongbum (Henry) Kim

Authors
  1. Hui Kwon Kim

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  2. Seonwoo Min

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  3. Myungjae Song

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  4. Soobin Jung

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  5. Jae Woo Choi

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  6. Younggwang Kim

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  7. Sangeun Lee

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  8. Sungroh Yoon

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  9. Hyongbum (Henry) Kim

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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.

Corresponding authors

Correspondence toSungroh Yoon orHyongbum (Henry) Kim.

Ethics declarations

Competing interests

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 Biotechnol36, 239–241 (2018). https://doi.org/10.1038/nbt.4061

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