Goloco:一个web应用程序,可以从非常小的实验中创建基因组规模的信息。

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Sajid M Hossain, Yiyun Rao, Jahid O Hossain, Justin R Pritchard, Boyang Zhao
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引用次数: 0

摘要

背景:功能基因组学旨在通过使用CRISPR技术观察特定基因被破坏时细胞的变化来破译基因功能。然而,这些实验受到可扩展性的限制,因为全面的CRISPR筛选需要大量的资源,涉及数百万个细胞和数千个sgrna,这使得大规模研究具有挑战性。我们提出了一种“CRISPR有损压缩”的新方法,通过关注可以推断全基因组表型的关键遗传节点来降低CRISPR筛选的复杂性。这些包含100到1000个基因的浓缩集合,使以前不切实际的全基因组筛选变得容易处理。结果:为了使这种方法更广泛地应用于科学界,我们开发了goloco,这是一个交互式web应用程序,允许用户从少至100个汇总测量中探索基因组尺度的功能丧失表型。该工具辅以广泛的分析,包括火山图可视化,回归和网络分析。结论:这个工具goloco使研究人员能够以最小的实验开销进行基因组规模的功能研究,扩大了大规模功能基因组学研究的可及性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
goloco: a web application to create genome scale information from surprisingly small experiments.

Background: Functional genomics aims to decipher gene function by observing cellular changes when specific genes are disrupted using CRISPR technology. However, these experiments are limited by scalability, as comprehensive CRISPR screens require extensive resources, involving millions of cells and thousands of sgRNAs, making large-scale studies challenging. We propose a novel approach with "CRISPR lossy compression" to reduce the complexity of CRISPR screens by focusing on key genetic nodes that can infer genome-wide phenotypes. These condensed sets, comprising 100 to 1,000 genes, enable previously impractical genome-wide screens tractable.

Results: To make this approach accessible to the wider scientific community, we developed goloco, an interactive web application that allows users to explore genome-scale loss-of-function phenotypes from as few as 100 pooled measurements. The tool is complemented by a wide array of analyses, including volcano plot visualizations, regression and network analyses.

Conclusions: This tool goloco empowers researchers to conduct genome-scale functional studies with minimal experimental overhead, broadening the accessibility of large-scale functional genomics research.

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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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