IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v10y2019i1d10.1038_s41467-019-12281-8.html
   My bibliography  Save this article

Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning

Author

Listed:
  • Daqi Wang

    (Fudan University)

  • Chengdong Zhang

    (Fudan University)

  • Bei Wang

    (Fudan University)

  • Bin Li

    (Fudan University)

  • Qiang Wang

    (Fudan University)

  • Dong Liu

    (Nantong University)

  • Hongyan Wang

    (Fudan University
    Fudan University)

  • Yan Zhou

    (Fudan University)

  • Leming Shi

    (Fudan University
    Fudan University)

  • Feng Lan

    (Capital Medical University)

  • Yongming Wang

    (Fudan University)

Abstract

Highly specific Cas9 nucleases derived from SpCas9 are valuable tools for genome editing, but their wide applications are hampered by a lack of knowledge governing guide RNA (gRNA) activity. Here, we perform a genome-scale screen to measure gRNA activity for two highly specific SpCas9 variants (eSpCas9(1.1) and SpCas9-HF1) and wild-type SpCas9 (WT-SpCas9) in human cells, and obtain indel rates of over 50,000 gRNAs for each nuclease, covering ~20,000 genes. We evaluate the contribution of 1,031 features to gRNA activity and develope models for activity prediction. Our data reveals that a combination of RNN with important biological features outperforms other models for activity prediction. We further demonstrate that our model outperforms other popular gRNA design tools. Finally, we develop an online design tool DeepHF for the three Cas9 nucleases. The database, as well as the designer tool, is freely accessible via a web server, http://www.DeepHF.com/ .

Suggested Citation

  • Daqi Wang & Chengdong Zhang & Bei Wang & Bin Li & Qiang Wang & Dong Liu & Hongyan Wang & Yan Zhou & Leming Shi & Feng Lan & Yongming Wang, 2019. "Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12281-8
    DOI: 10.1038/s41467-019-12281-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-019-12281-8
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-019-12281-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dipankar Baisya & Adithya Ramesh & Cory Schwartz & Stefano Lonardi & Ian Wheeldon, 2022. "Genome-wide functional screens enable the prediction of high activity CRISPR-Cas9 and -Cas12a guides in Yarrowia lipolytica," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Chengdong Zhang & Yuan Yang & Tao Qi & Yuening Zhang & Linghui Hou & Jingjing Wei & Jingcheng Yang & Leming Shi & Sang-Ging Ong & Hongyan Wang & Hui Wang & Bo Yu & Yongming Wang, 2023. "Prediction of base editor off-targets by deep learning," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    3. Stephan Riesenberg & Nelly Helmbrecht & Philipp Kanis & Tomislav Maricic & Svante Pääbo, 2022. "Improved gRNA secondary structures allow editing of target sites resistant to CRISPR-Cas9 cleavage," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    4. Nicolae Sapoval & Amirali Aghazadeh & Michael G. Nute & Dinler A. Antunes & Advait Balaji & Richard Baraniuk & C. J. Barberan & Ruth Dannenfelser & Chen Dun & Mohammadamin Edrisi & R. A. Leo Elworth &, 2022. "Current progress and open challenges for applying deep learning across the biosciences," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    5. Giulia I. Corsi & Kunli Qu & Ferhat Alkan & Xiaoguang Pan & Yonglun Luo & Jan Gorodkin, 2022. "CRISPR/Cas9 gRNA activity depends on free energy changes and on the target PAM context," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12281-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.