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Open Access Research article

Genetic architecture of gene expression in the chicken

Dragana Stanley124*, Nathan S Watson-Haigh3, Christopher JE Cowled1 and Robert J Moore12

Author Affiliations

1 CSIRO Animal, Food and Helath Sciences, Australian Animal Health Laboratories, Geelong, VIC, 3220, Australia

2 Poultry Cooperative Research Centre, PO Box U242, University of New England, Armidale, NSW, 2315, Australia

3 The Australian Wine Research Institute, Waite Precinct, Adelaide, SA, 5064, Australia

4 Central Queensland University, Higher Education Division, Bruce Highway, Rockhampton, QLD, 4702, Australia

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BMC Genomics 2013, 14:13  doi:10.1186/1471-2164-14-13

Published: 16 January 2013

Abstract

Background

The annotation of many genomes is limited, with a large proportion of identified genes lacking functional assignments. The construction of gene co-expression networks is a powerful approach that presents a way of integrating information from diverse gene expression datasets into a unified analysis which allows inferences to be drawn about the role of previously uncharacterised genes. Using this approach, we generated a condition-free gene co-expression network for the chicken using data from 1,043 publically available Affymetrix GeneChip Chicken Genome Arrays. This data was generated from a diverse range of experiments, including different tissues and experimental conditions. Our aim was to identify gene co-expression modules and generate a tool to facilitate exploration of the functional chicken genome.

Results

Fifteen modules, containing between 24 and 473 genes, were identified in the condition-free network. Most of the modules showed strong functional enrichment for particular Gene Ontology categories. However, a few showed no enrichment. Transcription factor binding site enrichment was also noted.

Conclusions

We have demonstrated that this chicken gene co-expression network is a useful tool in gene function prediction and the identification of putative novel transcription factors and binding sites. This work highlights the relevance of this methodology for functional prediction in poorly annotated genomes such as the chicken.