GLiDe: a web-based genome-scale CRISPRi sgRNA design tool for prokaryotes.

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Tongjun Xiang, Huibao Feng, Xin-Hui Xing, Chong Zhang
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Abstract

Background: CRISPRi screening has become a powerful approach for functional genomic research. However, the off-target effects resulting from the mismatch tolerance between sgRNAs and their intended targets is a primary concern in CRISPRi applications.

Results: We introduce Guide Library Designer (GLiDe), a web-based tool specifically created for the genome-scale design of sgRNA libraries tailored for CRISPRi screening in prokaryotic organisms. GLiDe incorporates a robust quality control framework, rooted in prior experimental knowledge, ensuring the accurate identification of off-target hits. It boasts an extensive built-in database, encompassing 1,397 common prokaryotic species as a comprehensive design resource. It also provides the capability to design sgRNAs for newly discovered organisms by accepting uploaded design resource. We further demonstrated that GLiDe exhibits enhanced precision in identifying off-target binding sites for the CRISPRi system.

Conclusions: We present a web server that allows the construction of genome-scale CRISPRi sgRNA libraries for prokaryotes. It mitigates off-target effects through a robust quality control framework, leveraging prior experimental knowledge within an end-to-end, user-friendly pipeline.

GLiDe:基于网络的原核生物基因组级CRISPRi sgRNA设计工具。
背景:CRISPRi筛选已成为功能基因组研究的有力手段。然而,由sgrna与其预期靶标之间的错配耐受性引起的脱靶效应是CRISPRi应用中的主要问题。结果:我们介绍了Guide Library Designer (GLiDe),这是一个基于网络的工具,专门为原核生物中CRISPRi筛选定制的sgRNA文库的基因组规模设计而创建。GLiDe结合了一个强大的质量控制框架,植根于先前的实验知识,确保准确识别脱靶命中。它拥有一个广泛的内置数据库,包括1397种常见的原核生物作为一个全面的设计资源。它还提供了通过接受上传的设计资源为新发现的生物体设计sgrna的能力。我们进一步证明,GLiDe在识别CRISPRi系统的脱靶结合位点方面具有更高的精度。结论:我们提出了一个web服务器,允许构建基因组规模的原核生物CRISPRi sgRNA文库。它通过强大的质量控制框架,在端到端用户友好的管道中利用先前的实验知识,减轻了脱靶效应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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