Wang Grant, Liu Xiaona, Wang Aoqi, Wen Jianguo, Kim Pora, Song Qianqian, Liu Xiaona, Zhou Xiaobo
Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX 77030, USA.
West China Biomedical Big Data Center, West China Hospital, Sichuan University, 2222 Xinchuan Road, Chengdu, Sichuan, 610041, PR China.
Nucleic Acids Res. 2025 Jan 6;53(D1):D914-D924. doi: 10.1093/nar/gkae1025.
The CRISPR (clustered regularly interspaced short palindromic repeats)/Cas (CRISPR-associated protein) programmable nuclease system continues to evolve, with in vivo therapeutic gene editing increasingly applied in clinical settings. However, off-target effects remain a significant challenge, hindering its broader clinical application. To enhance the development of gene-editing therapies and the accuracy of prediction algorithms, we developed CRISPRoffT (https://ccsm.uth.edu/CRISPRoffT/). Users can access a comprehensive repository of off-target regions predicted and validated by a diverse range of technologies across various cell lines, Cas enzyme variants, engineered sgRNAs (single guide RNAs) and CRISPR editing systems. CRISPRoffT integrates results of off-target analysis from 74 studies, encompassing 29 experimental prediction techniques, 368 guide sequences, 226 164 potential guide and off-target pairs and 8840 validated off-targets. CRISPRoffT features off-target data from different CRISPR approaches (knockout, base editing and prime editing) applied under diverse experimental conditions, including 85 different Cas/guide RNA (gRNA) combinations used across 34 different human and mouse cell lines. CRISPRoffT provides results of comparative analyses for individual guide sequences, genes, cell types, techniques and Cas/gRNA combinations under different conditions. CRISPRoffT is a unique resource providing valuable insights that facilitate the safety-driven design of CRISPR-based therapeutics, inform experimental design, advance the development of computational off-target prediction algorithms and guide RNA design algorithms.
CRISPR(成簇规律间隔短回文重复序列)/Cas(CRISPR相关蛋白)可编程核酸酶系统不断发展,体内治疗性基因编辑在临床环境中的应用越来越广泛。然而,脱靶效应仍然是一个重大挑战,阻碍了其更广泛的临床应用。为了促进基因编辑疗法的发展和预测算法的准确性,我们开发了CRISPRoffT(https://ccsm.uth.edu/CRISPRoffT/)。用户可以访问一个综合数据库,其中包含通过多种技术在各种细胞系、Cas酶变体、工程化sgRNA(单向导RNA)和CRISPR编辑系统中预测和验证的脱靶区域。CRISPRoffT整合了74项研究的脱靶分析结果,涵盖29种实验预测技术、368个引导序列、226164个潜在引导和脱靶对以及8840个经过验证的脱靶。CRISPRoffT具有在不同实验条件下应用的不同CRISPR方法(敲除、碱基编辑和引导编辑)的脱靶数据,包括在34种不同的人类和小鼠细胞系中使用的85种不同的Cas/引导RNA(gRNA)组合。CRISPRoffT提供了在不同条件下对单个引导序列、基因、细胞类型、技术和Cas/gRNA组合的比较分析结果。CRISPRoffT是一个独特的资源,提供了有价值的见解,有助于基于CRISPR的疗法的安全驱动设计,为实验设计提供信息,推进计算脱靶预测算法和引导RNA设计算法的发展。