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

文献类型： 外文期刊

第一作者： Yang, Qianqian

作者： Yang, Qianqian;Wu, Leilei;Meng, Juan;Sun, Yidi;Yang, Qianqian;Ma, Lei;Zuo, Erwei

作者机构：

关键词： CRISPR-mediated epigenome editing; Deep learning; Convolutional neural network; EpiCas-DL; Gene silencing; Gene activation

期刊名称：COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL （ 影响因子：6.0； 五年影响因子：6.3 ）

ISSN： 2001-0370

年卷期： 2023 年 21 卷

页码：

收录情况： SCI

摘要： 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. (c) 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

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