GuidePro: a multi-source ensemble predictor for prioritizing sgRNAs in CRISPR/Cas9 protein knockouts

被引:6
作者
He, Wei [1 ]
Wang, Helen [1 ]
Wei, Yanjun [2 ]
Jiang, Zhiyun [1 ]
Tang, Yitao [1 ]
Chen, Yiwen [2 ]
Xu, Han [1 ,2 ]
机构
[1] Univ Texas MD Anderson Canc Ctr, Dept Epigenet & Mol Carcinogenesis, Smithville, TX 78957 USA
[2] Univ Texas MD Anderson Canc Ctr, Dept Bioinformat & Computat Biol, Houston, TX 77030 USA
关键词
WEB SERVER; DESIGN;
D O I
10.1093/bioinformatics/btaa1068
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Motivation: The efficiency of CRISPR/Cas9-mediated protein knockout is determined by three factors: sequence-specific sgRNA activity, frameshift probability and the characteristics of targeted amino acids. A number of computational methods have been developed for predicting sgRNA efficiency from different perspectives. However, an integrative method that combines all three factors for rational sgRNA selection is still lacking. Results: We developed GuidePro, a two-layer ensemble predictor that enables the integration of multiple factors for the prioritization of sgRNAs in protein knockouts. Tested on independent datasets, GuidePro outperforms existing methods and demonstrates consistent superior performance in predicting phenotypes caused by protein loss-of-function, suggesting its robustness for prioritizing sgRNAs in various applications of CRISPR/Cas9 knockouts.
引用
收藏
页码:134 / 136
页数:3
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