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6 - Joining the dots: network analysis of gene perturbation data

Published online by Cambridge University Press:  05 July 2015

By
Xin Wang ,
Ke Yuan  and
Florian Markowetz
Edited by
Florian Markowetz  and
Michael Boutros
Xin Wang
Affiliation:
Cancer Research UK Cambridge Institute
Ke Yuan
Affiliation:
Cancer Research UK Cambridge Institute
Florian Markowetz
Affiliation:
Cancer Research UK Cambridge Institute
Florian Markowetz
Affiliation:
Cancer Research UK Cambridge Institute
Michael Boutros
Affiliation:
German Cancer Research Center, Heidelberg
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Summary

How to link genotypes and phenotypes is a long-standing question in modern biology. Modern high-throughput approaches are key technologies at the forefront of genetic research. They enable the analysis of a biological response to thousands of experimental perturbations and require a tight collaboration between experimental and computational scientists. Perturbation studies and computational approaches have revolutionized research in functional genomics and genetics and promise to lay the foundation for personalized medicine. For modern high-throughput technologies, computation is as important as experimentation. Genome-wide image-based RNA interference (RNAi) screens, for example, are only feasible because of computational techniques. Computational skills to analyse the data have become as important as experimental skills to generate the data.

Design and analysis of phenol typing screens depend on the number of genes perturbed and the richness of the phenotype observed (Figure 6.1). At one extreme are high-throughput screens with single reporters, e.g. a genome-wide screen for new components of a pathway. At the other extreme are perturbations of individual genes with very rich phenotypes, e.g. assessing the effects of a single gene perturbation on several molecular levels over time. Between these two extremes lie a variety of possible screen designs. Two widely used scenarios are small-scale perturbations (<20 genes) of a single target pathway with rich readouts, e.g. a global transcriptional profile, and medium-scale perturbations (hundreds of genes) with multi-parametric readouts, e.g. cell morphology or growth in different media. In the following we will discuss statistical and computational methodologies for functional analysis in all four scenarios.

Scenario 1: Genome-wide screens with single reporters

RNAi screens have been frequently and successfully applied for functional profiling of genes on a large scale (Boutros & Ahringer 2008). The vast majority of these applications use a single phenotype (e.g. cell viability, growth rate, activity of reporter constructs) to characterize the function of genes in specific biological pathways.

Type
Chapter
Information
Systems Genetics
Linking Genotypes and Phenotypes
 , pp. 83 - 107
Publisher: Cambridge University Press
Print publication year: 2015

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