Bioinformatics Advance Access published online on March 23, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm111
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Using DNA microarrays to study gene expression in closely related species
1 Walter and Eliza Hall Institute of Medical Research, Parkville, Vic, Australia 3050
2 Department of Human Genetics, University of Chicago, Chicago, IL 60605
*To whom correspondence should be addressed. Yoav Gilad, E-mail: gilad{at}uchicago.edu
| 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 a discernible loss of information.
Associate Editor: Prof. Martin Bishop
Received on January 9, 2007; revised on February 25, 2007; accepted on March 14, 2007
This article has been cited by other articles:
![]() |
H. E. Machado, A. A. Pollen, H. A. Hofmann, and S. C.P. Renn Interspecific profiling of gene expression informed by comparative genomic hybridization: A review and a novel approach in African cichlid fishes Integr. Comp. Biol., December 1, 2009; 49(6): 644 - 659. [Abstract] [Full Text] [PDF] |
||||
![]() |
L. Lin, S. Liu, H. Brockway, J. Seok, P. Jiang, W. H. Wong, and Y. Xing Using high-density exon arrays to profile gene expression in closely related species Nucleic Acids Res., July 1, 2009; 37(12): e90 - e90. [Abstract] [Full Text] [PDF] |
||||
![]() |
Y. Lu, P. Huggins, and Z. Bar-Joseph Cross species analysis of microarray expression data Bioinformatics, June 15, 2009; 25(12): 1476 - 1483. [Abstract] [Full Text] [PDF] |
||||
![]() |
S. Priyanka, P. Jayaram, R. Sridaran, and R. Medhamurthy Genome-Wide Gene Expression Analysis Reveals a Dynamic Interplay between Luteotropic and Luteolytic Factors in the Regulation of Corpus Luteum Function in the Bonnet Monkey (Macaca radiata) Endocrinology, March 1, 2009; 150(3): 1473 - 1484. [Abstract] [Full Text] [PDF] |
||||
![]() |
T. Bedford and D. L. Hartl Optimization of gene expression by natural selection PNAS, January 27, 2009; 106(4): 1133 - 1138. [Abstract] [Full Text] [PDF] |
||||
![]() |
R. Chaix, M. Somel, D. P. Kreil, P. Khaitovich, and G. A. Lunter Evolution of Primate Gene Expression: Drift and Corrective Sweeps? Genetics, November 1, 2008; 180(3): 1379 - 1389. [Abstract] [Full Text] [PDF] |
||||





