European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK.
Cell Surface Signalling Laboratory, Wellcome Sanger Institute, Cambridge, UK.
Mol Syst Biol. 2020 Oct;16(10):e9698. doi: 10.15252/msb.20209698.
An emerging theme from large-scale genetic screens that identify genes essential for cell fitness is that essentiality of a given gene is highly context-specific. Identification of such contexts could be the key to defining gene function and also to develop novel therapeutic interventions. Here, we present Context-specific Essentiality Network-tools (CEN-tools), a website and python package, in which users can interrogate the essentiality of a gene from large-scale genome-scale CRISPR screens in a number of biological contexts including tissue of origin, mutation profiles, expression levels and drug responses. We show that CEN-tools is suitable for the systematic identification of genetic dependencies and for more targeted queries. The associations between genes and a given context are represented as dependency networks (CENs), and we demonstrate the utility of these networks in elucidating novel gene functions. In addition, we integrate the dependency networks with existing protein-protein interaction networks to reveal context-dependent essential cellular pathways in cancer cells. Together, we demonstrate the applicability of CEN-tools in aiding the current efforts to define the human cellular dependency map.
从大规模基因筛选中出现的一个主题是,确定细胞适应性所必需的基因在很大程度上是特定于上下文的。识别这些上下文可能是定义基因功能的关键,也是开发新的治疗干预措施的关键。在这里,我们介绍了 Context-specific essentiality Network-tools(CEN-tools),这是一个网站和 Python 包,用户可以在许多生物学背景下(包括起源组织、突变谱、表达水平和药物反应)询问大规模基因组规模 CRISPR 筛选中基因的必需性。我们表明,CEN-tools 适用于系统地识别遗传依赖性和更有针对性的查询。基因与给定上下文之间的关联表示为依赖关系网络(CEN),我们证明了这些网络在阐明新的基因功能方面的实用性。此外,我们将依赖关系网络与现有的蛋白质-蛋白质相互作用网络集成在一起,以揭示癌细胞中依赖于上下文的基本细胞途径。总之,我们证明了 CEN-tools 在帮助当前定义人类细胞依赖图的努力中的适用性。