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Bioinformatics Advance Access originally published online on March 23, 2007
Bioinformatics 2007 23(10):1235-1242; doi:10.1093/bioinformatics/btm111
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Using DNA microarrays to study gene expression in closely related species

Alicia Oshlack 1,*, Adrien E. Chabot 2, Gordon K. Smyth 1 and Yoav Gilad 2,*

1Walter and Eliza Hall Institute of Medical Research, Parkville, Vic 3050, Australia and 2Department of Human Genetics, University of Chicago, Chicago, IL 60605, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Comparisons of gene expression levels within and between species have become a central tool in the study of the genetic basis for phenotypic variation, as well as in the study of the evolution of gene regulation. DNA microarrays are a key technology that enables these studies. Currently, however, microarrays are only available for a small number of species. Thus, in order to study gene expression levels in species for which microarrays are not available, researchers face three sets of choices: (i) use a microarray designed for another species, but only compare gene expression levels within species, (ii) construct a new microarray for every species whose gene expression profiles will be compared or (iii) build a multi-species microarray with probes from each species of interest. Here, we use data collected using a multi-primate cDNA array to evaluate the reliability of each approach.

Results: We find that, for inter-species comparisons, estimates of expression differences based on multi-species microarrays are more accurate than those based on multiple species-specific arrays. We also demonstrate that within-species expression differences can be estimated using a microarray for a closely related species, without discernible loss of information.

Contact: A.O. (oshlack{at}wehi.edu.au) or Y.G. (gilad{at}uchicago.edu)

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Martin Bishop

Received on January 9, 2006; revised on February 25, 2007; accepted on March 14, 2007

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