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CRISPRoffT:CRISPR/Cas脱靶效应的综合数据库。

CRISPRoffT: comprehensive database of CRISPR/Cas off-targets.

作者信息

Wang Grant, Liu Xiaona, Wang Aoqi, Wen Jianguo, Kim Pora, Song Qianqian, Liu Xiaona, Zhou Xiaobo

机构信息

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.

出版信息

Nucleic Acids Res. 2025 Jan 6;53(D1):D914-D924. doi: 10.1093/nar/gkae1025.

Abstract

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.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b03/11701555/be20e11d94d9/gkae1025figgra1.jpg

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