Journal
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
Volume 18, Issue -, Pages 35-44Publisher
ELSEVIER
DOI: 10.1016/j.csbj.2019.11.006
Keywords
CRISPR; Guide RNA design; Efficiency; Specificity; Machine-learning
Funding
- National Transgenic Major Project [2019ZX08010003-001-002, 2018ZX08020-003]
- Jiangsu Province Government Project/The open funds of the Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding [BK2018003, PL201801]
- Fund of Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
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The Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)/CRISPR-associated (Cas) system has emerged as the main technology for gene editing. Successful editing by CRISPR requires an appropriate Cas protein and guide RNA. However, low cleavage efficiency and off-target effects hamper the development and application of CRISPR/Cas systems. To predict cleavage efficiency and specificity, numerous computational approaches have been developed for scoring guide RNAs. Most scores are empirical or trained by experimental datasets, and scores are implemented using various computational methods. Herein, we discuss these approaches, focusing mainly on the features or computational methods they utilise. Furthermore, we summarise these tools and give some suggestions for their usage. We also recommend three versatile web-based tools with user-friendly interfaces and preferable functions. The review provides a comprehensive and up-to-date overview of computational approaches for guide RNA design that could help users to select the optimal tools for their research. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
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