Abstract
Modern high-throughput gene perturbation screens are key technologies at the forefront of genetic research. Combined with rich phenotypic descriptors they enable researchers to observe detailed cellular reactions to experimental perturbations on a genome-wide scale. This review surveys the current state-of-the-art in analyzing perturbation screens from a network point of view. We describe approaches to make the step from the parts list to the wiring diagram by using phenotypes for network inference and integrating them with complementary data sources. The first part of the review describes methods to analyze one- or low-dimensional phenotypes like viability or reporter activity; the second part concentrates on high-dimensional phenotypes showing global changes in cell morphology, transcriptome or proteome.
Highlights
Functional genomics has demonstrated considerable success in inferring the inner working of a cell through analysis of its response to various perturbations
In this review we have discussed two main approaches to describe the reaction of a cell to an experimental gene perturbation: low-dimensional phenotypes measure individual reporters for cell viability or pathway activation, while high-dimensional phenotypes show global effects on cell morphology, transcriptome, or proteome
All of them can be directly applied to gene perturbation screens, even though some of them have been introduced in different contexts
Summary
Functional genomics has demonstrated considerable success in inferring the inner working of a cell through analysis of its response to various perturbations. The central difference is that high-dimensional phenotypes allow one to compute correlations and other similarity measures, which are not applicable for low-dimensional phenotypes Another important distinction is between static phenotypes, providing a Citation: Markowetz F (2010) How to Understand the Cell by Breaking It: Network Analysis of Gene Perturbation Screens. Different protein activation states by phosphorylation may not be visible by changes in mRNA concentrations (see Figure 1B) This gap between observed phenotypes and underlying cellular networks is the main problem in the analysis of perturbation screens and applies to both low- and highdimensional screens. The following methods address the challenge in different ways, mostly by integrating the perturbation effects and phenotypes with additional sources of information like collections of functionally related gene sets or protein-interaction networks. Clustering and ranking can be combined with enrichment analysis (as discussed above) for functional interpretation
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