Kasidet Hiranniramol1, Yuhao Chen1,2, Weijun Liu1,3, Xiaowei Wang1. 1. Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA. 2. Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA. 3. Nawgen LLC, St. Louis, MO, USA.
Abstract
MOTIVATION: The development of clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) technology has provided a simple yet powerful system for targeted genome editing. In recent years, this system has been widely used for various gene editing applications. The CRISPR editing efficacy is mainly dependent on the single guide RNA (sgRNA), which guides Cas9 for genome cleavage. While there have been multiple attempts at improving sgRNA design, there is a pressing need for greater sgRNA potency and generalizability across various experimental conditions. RESULTS: We employed a unique plasmid library expressed in human cells to quantify the potency of thousands of CRISPR/Cas9 sgRNAs. Differential sequence and structural features among the most and least potent sgRNAs were then used to train a machine learning algorithm for assay design. Comparative analysis indicates that our new algorithm outperforms existing CRISPR/Cas9 sgRNA design tools. AVAILABILITY AND IMPLEMENTATION: The new sgRNA design tool is freely accessible as a web application, http://crispr.wustl.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: The development of clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) technology has provided a simple yet powerful system for targeted genome editing. In recent years, this system has been widely used for various gene editing applications. The CRISPR editing efficacy is mainly dependent on the single guide RNA (sgRNA), which guides Cas9 for genome cleavage. While there have been multiple attempts at improving sgRNA design, there is a pressing need for greater sgRNA potency and generalizability across various experimental conditions. RESULTS: We employed a unique plasmid library expressed in human cells to quantify the potency of thousands of CRISPR/Cas9 sgRNAs. Differential sequence and structural features among the most and least potent sgRNAs were then used to train a machine learning algorithm for assay design. Comparative analysis indicates that our new algorithm outperforms existing CRISPR/Cas9 sgRNA design tools. AVAILABILITY AND IMPLEMENTATION: The new sgRNA design tool is freely accessible as a web application, http://crispr.wustl.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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