Genome Biology (Jun 2018)

DeepCRISPR: optimized CRISPR guide RNA design by deep learning

  • 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

DOI
https://doi.org/10.1186/s13059-018-1459-4
Journal volume & issue
Vol. 19, no. 1
pp. 1 – 18

Abstract

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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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