DeepCRISPR: optimized CRISPR guide RNA design by deep learning

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 comprehen...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6020378/
https://www.ncbi.nlm.nih.gov/pubmed/29945655
http://dx.doi.org/10.1186/s13059-018-1459-4
Descripción
Sumario: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/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-018-1459-4) contains supplementary material, which is available to authorized users.

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