DeepCRISPR: optimized CRISPR guide RNA design by deep learning

• Published in: Genome Biology Vol. 19; no. 1; p. 80
• Main Authors: Chuai, Guohui, Ma, Hanhui, Yan, Jifang, Chen, Ming, Hong, Nanfang, Xue, Dongyu, Zhou, Chi, Zhu, Chenyu, Chen, Ke, Duan, Bin, Gu, Feng, Qu, Sheng, Huang, Deshuang, Wei, Jia, Liu, Qi
• Format: Journal Article
• Language: English
• Published: England BioMed Central 26.06.2018; BMC
• Subjects: Cell Line; Cell Line, Tumor; Clustered Regularly Interspaced Short Palindromic Repeats - genetics; Computational Biology - methods; Computer applications; Computer Simulation; CRISPR; CRISPR system; CRISPR-Cas Systems - genetics; Data processing; Deep learning; Deoxyribonucleic acid; Design; DNA; Epigenetics; Gene knockout; genome; Genomes; HCT116 Cells; HEK293 Cells; HeLa Cells; HL-60 Cells; Humans; Hypotheses; Learning algorithms; Machine Learning; Method; Off-targets; On-targets; prediction; Ribonucleic acid; RNA; RNA Editing - genetics; RNA, Guide, CRISPR-Cas Systems - genetics; Target recognition; Deep learning; On-targets; CRISPR system; Off-targets; Gene knockout
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-018-1459-4

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/ .