How to Understand the Cell by Breaking It: Network Analysis of Gene Perturbation Screens

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 stateof-the-art in analyzing single gene 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.

Dynamics of Cell Shape Inheritance in Fission Yeast

“…Conversely, our findings indicate that dynamical morphogenetic features can be used to infer morphogenetic network information even from single gene knockout data [57]. This may make such features particularly attractive in the context of genome-wide morphogenesis screens using yeast or other cell types like D. melanogaster S2 cells, as they might provide a means for obtaining some of the network information which could otherwise only be obtained with synthetic genetic interaction analysis by double RNAi [58].…”

Network Approaches to the Understanding of Alzheimer’s Disease: From Model Organisms to Humans

“…For genetic networks (synthetic-lethal or epistasis), interactions refl ect functional consequences of mutations, not direct physical mechanisms. Going forward, building a differential interaction map [ 28 ] will be the priority in order to understand the dysregulation of the pathways involved in AD.…”

“…Importantly as these pathways are present also in the healthy individual, we need to understand how they change and become dis-regulated in the diseased condition. We need in other words differential disease networks [ 28 ].…”

Single-cell and multivariate approaches in genetic perturbation screens

“…This progress is being captured by numerous databases that contain various types of information about functional associations between genes, including co-expression from microarrays, protein-protein interactions and manually curated pathways [121][122][123] . As a result, there is an increasing tendency to compare results from a large-scale genetic perturbation screen with such databases, and computational approaches are developed to use such databases as a priori information in the analyses of large-scale genetic perturbation screens 124,125 . For example, iterative feature selection can be applied to compare the clustering of gene perturbations against such databases, selecting or scaling features to improve overlap 126 .…”