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[Application of machine learning in the CRISPR/Cas9 system].

Authors :
Zhang GS
Yang Y
Zhang LM
Dai XH
Source :
Yi chuan = Hereditas [Yi Chuan] 2018 Sep 20; Vol. 40 (9), pp. 704-723.
Publication Year :
2018

Abstract

The third generation of the CRISPR/Cas9-mediated genome fixed-point editing technology has been widely used in the field of gene editing and gene expression regulation. How to improve the on-target efficiency and specificity of this system, as well as reduce its off-target effects are always the bottleneck in its development. Machine learning provides novel methods to the problems of the CRISPR/Cas9 system, and CRISPR/Cas9-based machine learning has recently become a very hot research topic. In this review, we firstly outline the mechanism of the CRISPR/Cas9 system. Subsequently, we elaborate the current issues of CRISPR/Cas9, including low efficiency and potential off-target effects, and sequence-recognizing limitation from protospacer adjacent motif (PAM). Finally, we summarize the applications of methods within the machine learning framework for optimizing the CRISPR/Cas9 system, such as optimized single-guide RNA (sgRNA) design, CRISPR/Cas9 cleavage efficiency prediction, off-target effects evaluation, gene knock-out as well as high-throughput functional genetic screening and prospects for development.

Details

Language :
Chinese
ISSN :
0253-9772
Volume :
40
Issue :
9
Database :
MEDLINE
Journal :
Yi chuan = Hereditas
Publication Type :
Academic Journal
Accession number :
30369475
Full Text :
https://doi.org/10.16288/j.yczz.18-135