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GNL-Scorer: a generalized model for predicting CRISPR on-target activity by machine learning and featurization
Jun Wang1,2 , Xi Xiang3,4 , Lars Bolund3,4,5 , Xiuqing Zhang1,3 , Lixin Cheng2,* , Yonglun Luo3,4,5,*
1BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, China
2Department of Critical Care Medicine, Shenzhen People’s Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China
3BGI-Shenzhen, Shenzhen, China
4Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, BGI-Shenzhen, Qingdao, China
5Department of Biomedicine, Aarhus University, Denmark
*Correspondence to:Lixin Cheng , Email:easonlcheng@gmail.com Yonglun Luo , Email:luoyonglun@genomics.cn
J Mol Cell Biol, Volume 12, Issue 11, November 2020, 909-911,  https://doi.org/10.1093/jmcb/mjz116

CRISPR/Cas9 is an adaptive immunity system in bacteria and most archaea (Koonin and Makarova, 2009; Horvath and Barrangou, 2010). The CRISPR/Cas9 gene editing system is comprised of two key components, a small guide RNA (gRNA) and a Cas9 endonuclease (Deltcheva et al., 2011; Jinek et al., 2012). The gRNA is a chimeric RNA molecule of tracrRNA and crRNA (Ran et al., 2013) which guides the Cas9 protein to the target site in the genome. Therefore, selecting a target site with high on-target activity and low off-target effect is crucial for gene editing. Previously, we have discovered that the gene editing activity of CRISPR-Cas9 in mammalian cells was affected by several factors, such as the secondary structure and chromatin accessibility of the guide sequences (Jensen et al., 2017). Strikingly, previous results from us and other research groups consistently revealed that the CRISPR gRNA activities are highly variable. Thus, several in silico gRNA design web tools and algorithms have been developed to facilitate CRISPR design and applications.