DeepCRISPR: optimized CRISPR guide RNA design by deep learning
2018
Guohui Chuai | Hanhui Ma | Jifang Yan | Ming Chen | Nanfang Hong | Dongyu Xue | Chi Zhou | Chenyu Zhu | Ke Chen | Bin Duan | Feng Gu | Sheng Qu | Deshuang Huang | Jia Wei | Qi Liu
Abstract A major challenge for effective application of CRISPR systems is to accurately predict the single guide RNA (sgRNA) on-target knockout efficacy and off-target profile, which would facilitate the optimized design of sgRNAs with high sensitivity and specificity. Here we present DeepCRISPR, a comprehensive computational platform to unify sgRNA on-target and off-target site prediction into one framework with deep learning, surpassing available state-of-the-art in silico tools. In addition, DeepCRISPR fully automates the identification of sequence and epigenetic features that may affect sgRNA knockout efficacy in a data-driven manner. DeepCRISPR is available at http://www.deepcrispr.net/.
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