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Gene networks in Drosophila melanogaster: integrating experimental data to predict gene function

James C Costello12, Mehmet M Dalkilic13, Scott M Beason1, Jeff R Gehlhausen1, Rupali Patwardhan34, Sumit Middha35, Brian D Eads2 and Justen R Andrews12*

Author affiliations

1 School of Informatics, Indiana University, E. Tenth St, Bloomington, Indiana 47408, USA

2 Department of Biology, Indiana University, E. Third St, Bloomington, Indiana 47405, USA

3 Center for Genomics and Bioinformatics, Indiana University, E. Third St., Bloomington, Indiana 47405, USA

4 Current address: Department of Genome Sciences, University of Washington, NE Pacific St, Seattle, Washington 98195-5065, USA

5 Current address: Bioinformatics Core, Mayo Clinic, First St SW, Rochester, Minnesota 55905, USA

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Citation and License

Genome Biology 2009, 10:R97  doi:10.1186/gb-2009-10-9-r97

Published: 16 September 2009

Abstract

Background

Discovering the functions of all genes is a central goal of contemporary biomedical research. Despite considerable effort, we are still far from achieving this goal in any metazoan organism. Collectively, the growing body of high-throughput functional genomics data provides evidence of gene function, but remains difficult to interpret.

Results

We constructed the first network of functional relationships for Drosophila melanogaster by integrating most of the available, comprehensive sets of genetic interaction, protein-protein interaction, and microarray expression data. The complete integrated network covers 85% of the currently known genes, which we refined to a high confidence network that includes 20,000 functional relationships among 5,021 genes. An analysis of the network revealed a remarkable concordance with prior knowledge. Using the network, we were able to infer a set of high-confidence Gene Ontology biological process annotations on 483 of the roughly 5,000 previously unannotated genes. We also show that this approach is a means of inferring annotations on a class of genes that cannot be annotated based solely on sequence similarity. Lastly, we demonstrate the utility of the network through reanalyzing gene expression data to both discover clusters of coregulated genes and compile a list of candidate genes related to specific biological processes.

Conclusions

Here we present the the first genome-wide functional gene network in D. melanogaster. The network enables the exploration, mining, and reanalysis of experimental data, as well as the interpretation of new data. The inferred annotations provide testable hypotheses of previously uncharacterized genes.