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Open Access Research article

Information-based methods for predicting gene function from systematic gene knock-downs

Matthew T Weirauch1, Christopher K Wong1, Alexandra B Byrne2 and Joshua M Stuart1*

Author Affiliations

1 Department of Biomolecular Engineering, 1156 High Street, Mail Stop: SOE2, University of California, Santa Cruz, CA 95064, USA

2 Department of Molecular Genetics, The Terrence Donnelly Centre for Cellular and Biomolecular Research, 160 College St., University of Toronto, Toronto, ON, M5S 3E1, Canada

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BMC Bioinformatics 2008, 9:463  doi:10.1186/1471-2105-9-463

Published: 29 October 2008

Abstract

Background

The rapid annotation of genes on a genome-wide scale is now possible for several organisms using high-throughput RNA interference assays to knock down the expression of a specific gene. To date, dozens of RNA interference phenotypes have been recorded for the nematode Caenorhabditis elegans. Although previous studies have demonstrated the merit of using knock-down phenotypes to predict gene function, it is unclear how the data can be used most effectively. An open question is how to optimally make use of phenotypic observations, possibly in combination with other functional genomics datasets, to identify genes that share a common role.

Results

We compared several methods for detecting gene-gene functional similarity from phenotypic knock-down profiles. We found that information-based measures, which explicitly incorporate a phenotype's genomic frequency when calculating gene-gene similarity, outperform non-information-based methods. We report the presence of newly predicted modules identified from an integrated functional network containing phenotypic congruency links derived from an information-based measure. One such module is a set of genes predicted to play a role in regulating body morphology based on their multiply-supported interactions with members of the TGF-β signaling pathway.

Conclusion

Information-based metrics significantly improve the comparison of phenotypic knock-down profiles, based upon their ability to enhance gene function prediction and identify novel functional modules.