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Revealing Molecular Mechanisms by Integrating High-Dimensional Functional Screens with Protein Interaction Data

机译:通过将高维功能屏幕与蛋白质相互作用数据整合来揭示分子机制

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Functional genomics screens using multi-parametric assays are powerful approaches for identifying genes involved in particular cellular processes. However, they suffer from problems like noise, and often provide little insight into molecular mechanisms. A bottleneck for addressing these issues is the lack of computational methods for the systematic integration of multi-parametric phenotypic datasets with molecular interactions. Here, we present Integrative Multi Profile Analysis of Cellular Traits (IMPACT). The main goal of IMPACT is to identify the most consistent phenotypic profile among interacting genes. This approach utilizes two types of external information: sets of related genes (IMPACT-sets) and network information (IMPACT-modules). Based on the notion that interacting genes are more likely to be involved in similar functions than non-interacting genes, this data is used as a prior to inform the filtering of phenotypic profiles that are similar among interacting genes. IMPACT-sets selects the most frequent profile among a set of related genes. IMPACT-modules identifies sub-networks containing genes with similar phenotype profiles. The statistical significance of these selections is subsequently quantified via permutations of the data. IMPACT (1) handles multiple profiles per gene, (2) rescues genes with weak phenotypes and (3) accounts for multiple biases e.g. caused by the network topology. Application to a genome-wide RNAi screen on endocytosis showed that IMPACT improved the recovery of known endocytosis-related genes, decreased off-target effects, and detected consistent phenotypes. Those findings were confirmed by rescreening 468 genes. Additionally we validated an unexpected influence of the IGF-receptor on EGF-endocytosis. IMPACT facilitates the selection of high-quality phenotypic profiles using different types of independent information, thereby supporting the molecular interpretation of functional screens.
机译:使用多参数分析的功能基因组学筛选是鉴定涉及特定细胞过程的基因的有效方法。然而,它们遭受诸如噪声之类的问题的困扰,并且通常对分子机制了解甚少。解决这些问题的瓶颈是缺乏具有分子相互作用的多参数表型数据集系统集成的计算方法。在这里,我们提出了细胞性状的综合多谱分析(IMPACT)。 IMPACT的主要目标是确定相互作用基因之间最一致的表型特征。这种方法利用了两种类型的外部信息:相关基因集(IMPACT集)和网络信息(IMPACT模块)。基于相互作用基因比非相互作用基因更可能参与相似功能的观点,该数据被用作先验信息,以过滤相互作用基因之间相似的表型谱。 IMPACT-sets在一组相关基因中选择最常见的配置文件。 IMPACT模块可识别包含具有相似表型特征基因的子网。这些选择的统计意义随后通过数据排列进行量化。 IMPACT(1)处理每个基因的多个图谱;(2)拯救具有弱表型的基因;(3)解决多种偏见,例如由网络拓扑引起。应用于内吞作用的全基因组RNAi筛选显示,IMPACT改善了已知的内吞作用相关基因的回收率,降低了脱靶效应,并检测出一致的表型。通过重新筛选468个基因,证实了这些发现。此外,我们验证了IGF受体对EGF内吞作用的意外影响。 IMPACT有助于使用不同类型的独立信息来选择高质量的表型概况,从而支持功能屏幕的分子解释。

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