EpiCas-DL: Predicting sgRNA activity for CRISPR-mediated epigenome editing by deep learning

Authors：Yang, Qianqian; Wu, Leilei; Meng, Juan; Ma, Lei; Zuo, Erwei^*; Sun, Yidi^*

Source：Computational and Structural Biotechnology Journal, 2023, 21: 202-211.

DOI：10.1016/j.csbj.2022.11.034

• CRISPR-mediated epigenome editing
• Deep learning
• Convolutional neural network
• EpiCas-DL
• Gene silencing
• Gene activation

CRISPR-mediated epigenome editing enables gene expression regulation without changing the underlying DNA sequence, and thus has vast potential for basic research and gene therapy. Effective selection of a single guide RNA (sgRNA) with high on-target efficiency and specificity would facilitate the application of epigenome editing tools. Here we performed an extensive analysis of CRISPR-mediated epigenome editing tools on thousands of experimentally examined on-target sites and established EpiCas-DL, a deep learning framework to optimize sgRNA design for gene silencing or activation. EpiCas-DL achieves high accuracy in sgRNA activity prediction for targeted gene silencing or activation and outperforms other available in silico methods. In addition, EpiCas-DL also identifies both epigenetic and sequence features that affect sgRNA efficacy in gene silencing and activation, facilitating the application of epigenome editing for research and therapy. EpiCas-DL is available at http://www.sunlab.fun:3838/EpiCas-DL.

• Institution
  郑州大学; 中国科学院