Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

Open Access Methodology article

Operon information improves gene expression estimation for cDNA microarrays

Guanghua Xiao1*, Betsy Martinez-Vaz2, Wei Pan1 and Arkady B Khodursky2

Author Affiliations

1 Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, Minneapolis, MN 55455-0378, USA

2 Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Saint Paul, MN, 55108, USA

For all author emails, please log on.

BMC Genomics 2006, 7:87  doi:10.1186/1471-2164-7-87

Published: 21 April 2006

Abstract

Background

In prokaryotic genomes, genes are organized in operons, and the genes within an operon tend to have similar levels of expression. Because of co-transcription of genes within an operon, borrowing information from other genes within the same operon can improve the estimation of relative transcript levels; the estimation of relative levels of transcript abundances is one of the most challenging tasks in experimental genomics due to the high noise level in microarray data. Therefore, techniques that can improve such estimations, and moreover are based on sound biological premises, are expected to benefit the field of microarray data analysis

Results

In this paper, we propose a hierarchical Bayesian model, which relies on borrowing information from other genes within the same operon, to improve the estimation of gene expression levels and, hence, the detection of differentially expressed genes. The simulation studies and the analysis of experiential data demonstrated that the proposed method outperformed other techniques that are routinely used to estimate transcript levels and detect differentially expressed genes, including the sample mean and SAM t statistics. The improvement became more significant as the noise level in microarray data increases.

Conclusion

By borrowing information about transcriptional activity of genes within classified operons, we improved the estimation of gene expression levels and the detection of differentially expressed genes.