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Nucleic Acids Res. 2014 Dec 1;42(21). doi: 10.1093/nar/gku916. Epub 2014 Oct 7.

Large-scale modeling of condition-specific gene regulatory networks by information integration and inference.

Author information

1
Chair of Genome-Oriented Bioinformatics, Technische Universität München, Center of Life and Food Sciences Weihenstephan, 85354 Freising, Germany Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany ellwanger@wzw.tum.de.
2
Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany.
3
Chair of Genome-Oriented Bioinformatics, Technische Universität München, Center of Life and Food Sciences Weihenstephan, 85354 Freising, Germany Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany.

Abstract

Understanding how regulatory networks globally coordinate the response of a cell to changing conditions, such as perturbations by shifting environments, is an elementary challenge in systems biology which has yet to be met. Genome-wide gene expression measurements are high dimensional as these are reflecting the condition-specific interplay of thousands of cellular components. The integration of prior biological knowledge into the modeling process of systems-wide gene regulation enables the large-scale interpretation of gene expression signals in the context of known regulatory relations. We developed COGERE (http://mips.helmholtz-muenchen.de/cogere), a method for the inference of condition-specific gene regulatory networks in human and mouse. We integrated existing knowledge of regulatory interactions from multiple sources to a comprehensive model of prior information. COGERE infers condition-specific regulation by evaluating the mutual dependency between regulator (transcription factor or miRNA) and target gene expression using prior information. This dependency is scored by the non-parametric, nonlinear correlation coefficient η(2) (eta squared) that is derived by a two-way analysis of variance. We show that COGERE significantly outperforms alternative methods in predicting condition-specific gene regulatory networks on simulated data sets. Furthermore, by inferring the cancer-specific gene regulatory network from the NCI-60 expression study, we demonstrate the utility of COGERE to promote hypothesis-driven clinical research.

PMID:
25294834
PMCID:
PMC4245971
DOI:
10.1093/nar/gku916
[Indexed for MEDLINE]
Free PMC Article

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