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This article is part of the supplement: Selected Proceedings of the First Summit on Translational Bioinformatics 2008

Open Access Proceedings

Leveraging existing biological knowledge in the identification of candidate genes for facial dysmorphology

Hannah J Tipney1, Sonia M Leach12, Weiguo Feng3, Richard Spritz4, Trevor Williams3 and Lawrence Hunter1*

Author Affiliations

1 Computational Pharmacology Department, University of Colorado at Denver and Health Sciences Center, Aurora, CO, USA

2 ESAT, Research Division SCD, Katholieke Universiteit Leuven, B-3001 Leuven, Belgium

3 Department of Craniofacial Biology, University of Colorado at Denver and Health Sciences Center, Aurora, CO, USA

4 Human Medical Genetics Program, University of Colorado at Denver and Health Sciences Center, Aurora, CO, USA

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BMC Bioinformatics 2009, 10(Suppl 2):S12  doi:10.1186/1471-2105-10-S2-S12

Published: 5 February 2009



In response to the frequently overwhelming output of high-throughput microarray experiments, we propose a methodology to facilitate interpretation of biological data in the context of existing knowledge. Through the probabilistic integration of explicit and implicit data sources a functional interaction network can be constructed. Each edge connecting two proteins is weighted by a confidence value capturing the strength and reliability of support for that interaction given the combined data sources. The resulting network is examined in conjunction with expression data to identify groups of genes with significant temporal or tissue specific patterns. In contrast to unstructured gene lists, these networks often represent coherent functional groupings.


By linking from shared functional categorizations to primary biological resources we apply this method to craniofacial microarray data, generating biologically testable hypotheses and identifying candidate genes for craniofacial development.


The novel methodology presented here illustrates how the effective integration of pre-existing biological knowledge and high-throughput experimental data drives biological discovery and hypothesis generation.