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