Open Access Highly Accessed Research article

The functional landscape of mouse gene expression

Wen Zhang12, Quaid D Morris13, Richard Chang1, Ofer Shai3, Malina A Bakowski1, Nicholas Mitsakakis1, Naveed Mohammad1, Mark D Robinson1, Ralph Zirngibl2, Eszter Somogyi2, Nancy Laurin2, Eftekhar Eftekharpour4, Eric Sat5, Jörg Grigull1, Qun Pan1, Wen-Tao Peng1, Nevan Krogan12, Jack Greenblatt12, Michael Fehlings46, Derek van der Kooy2, Jane Aubin2, Benoit G Bruneau27, Janet Rossant25, Benjamin J Blencowe12, Brendan J Frey3 and Timothy R Hughes12*

Author Affiliations

1 Banting and Best Department of Medical Research, University of Toronto, 1 King's College Circle, Toronto, ON M5S 1A8, Canada

2 Department of Medical Genetics and Microbiology, University of Toronto, 1 King's College Circle, Toronto, ON M5S 1A8, Canada

3 Department of Electrical and Computer Engineering, University of Toronto, 1 King's College Circle, Toronto, ON M5S 1A8, Canada

4 Department of Surgery, University of Toronto, 1 King's College Circle, Toronto, ON M5S 1A8, Canada

5 Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, ON M5G 1X5, Canada

6 Division of Cell and Molecular Biology, Toronto Western Research Institute and Krembil Neuroscience Center, 399 Bathurst St., Toronto, ON M5T 2S8, Canada

7 The Hospital for Sick Children, 555 University Ave., Toronto, ON M5G 1X8, Canada

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Journal of Biology 2004, 3:21  doi:10.1186/jbiol16

Published: 6 December 2004

Abstract

Background

Large-scale quantitative analysis of transcriptional co-expression has been used to dissect regulatory networks and to predict the functions of new genes discovered by genome sequencing in model organisms such as yeast. Although the idea that tissue-specific expression is indicative of gene function in mammals is widely accepted, it has not been objectively tested nor compared with the related but distinct strategy of correlating gene co-expression as a means to predict gene function.

Results

We generated microarray expression data for nearly 40,000 known and predicted mRNAs in 55 mouse tissues, using custom-built oligonucleotide arrays. We show that quantitative transcriptional co-expression is a powerful predictor of gene function. Hundreds of functional categories, as defined by Gene Ontology 'Biological Processes', are associated with characteristic expression patterns across all tissues, including categories that bear no overt relationship to the tissue of origin. In contrast, simple tissue-specific restriction of expression is a poor predictor of which genes are in which functional categories. As an example, the highly conserved mouse gene PWP1 is widely expressed across different tissues but is co-expressed with many RNA-processing genes; we show that the uncharacterized yeast homolog of PWP1 is required for rRNA biogenesis.

Conclusions

We conclude that 'functional genomics' strategies based on quantitative transcriptional co-expression will be as fruitful in mammals as they have been in simpler organisms, and that transcriptional control of mammalian physiology is more modular than is generally appreciated. Our data and analyses provide a public resource for mammalian functional genomics.