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

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/ .

journal_name

Nat Commun

journal_title

Nature communications

authors

Wang D,Zhang C,Wang B,Li B,Wang Q,Liu D,Wang H,Zhou Y,Shi L,Lan F,Wang Y

doi

10.1038/s41467-019-12281-8

subject

Has Abstract

pub_date

2019-09-19 00:00:00

pages

4284

issue

1

issn

2041-1723

pii

10.1038/s41467-019-12281-8

journal_volume

10

pub_type

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