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Open Access Methodology article

Extracting quantitative genetic interaction phenotypes from matrix combinatorial RNAi

Elin Axelsson12*, Thomas Sandmann34, Thomas Horn35, Michael Boutros3, Wolfgang Huber12 and Bernd Fischer12

Author Affiliations

1 EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK

2 Genome Biology Unit, EMBL, Meyerhofstraße 1, D-69117 Heidelberg, Germany

3 German Cancer Research Center (DKFZ), Div. Signaling and Functional Genomics and Department of Cell and Molecular Biology, Faculty of Medicine Mannheim, Heidelberg University, Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany

4 CellNetworks Cluster of Excellence, Heidelberg University, Im Neuenheimer Feld 267, D-69120 Heidelberg, Germany

5 Hartmut Hoffmann-Berling International Graduate School (HBIGS), Heidelberg University, Im Neuenheimer Feld 501, D-69120 Heidelberg, Germany

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BMC Bioinformatics 2011, 12:342  doi:10.1186/1471-2105-12-342

Published: 17 August 2011

Abstract

Background

Systematic measurement of genetic interactions by combinatorial RNAi (co-RNAi) is a powerful tool for mapping functional modules and discovering components. It also provides insights into the role of epistasis on the way from genotype to phenotype. The interpretation of co-RNAi data requires computational and statistical analysis in order to detect interactions reliably and sensitively.

Results

We present a comprehensive approach to the analysis of univariate phenotype measurements, such as cell growth. The method is based on a quantitative model and is demonstrated on two example Drosophila cell culture data sets. We discuss adjustments for technical variability, data quality assessment, model parameter fitting and fit diagnostics, choice of scale, and assessment of statistical significance.

Conclusions

As a result, we obtain quantitative genetic interactions and interaction networks reflecting known biological relationships between target genes. The reliable extraction of presence, absence, and strength of interactions provides insights into molecular mechanisms.