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

Daqi Wang, Chengdong Zhang, Bei Wang, Bin Li, Qiang Wang, Dong Liu, Hongyan Wang, Yan Zhou, Leming Shi, Feng Lan () and Yongming Wang ()
Additional contact information
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
Yan Zhou: Fudan University
Leming Shi: Fudan University
Feng Lan: Capital Medical University
Yongming Wang: Fudan University

Nature Communications, 2019, vol. 10, issue 1, 1-14

Abstract: 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/ .

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.nature.com/articles/s41467-019-12281-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12281-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-12281-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().