The impact of quantitative optimization of hybridization conditions on gene expression analysis
- Equal contributors
1 Chair of Bioinformatics, Department of Biotechnology, Boku University, Vienna, A-1190 Muthgasse 18, Austria
2 Cambridge Systems Biology Centre, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK
3 Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK
4 Cambridge Computational Biology Institute, Dept of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
BMC Bioinformatics 2011, 12:73 doi:10.1186/1471-2105-12-73Published: 14 March 2011
With the growing availability of entire genome sequences, an increasing number of scientists can exploit oligonucleotide microarrays for genome-scale expression studies. While probe-design is a major research area, relatively little work has been reported on the optimization of microarray protocols.
As shown in this study, suboptimal conditions can have considerable impact on biologically relevant observations. For example, deviation from the optimal temperature by one degree Celsius lead to a loss of up to 44% of differentially expressed genes identified. While genes from thousands of Gene Ontology categories were affected, transcription factors and other low-copy-number regulators were disproportionately lost. Calibrated protocols are thus required in order to take full advantage of the large dynamic range of microarrays.
For an objective optimization of protocols we introduce an approach that maximizes the amount of information obtained per experiment. A comparison of two typical samples is sufficient for this calibration. We can ensure, however, that optimization results are independent of the samples and the specific measures used for calibration. Both simulations and spike-in experiments confirmed an unbiased determination of generally optimal experimental conditions.
Well calibrated hybridization conditions are thus easily achieved and necessary for the efficient detection of differential expression. They are essential for the sensitive pro filing of low-copy-number molecules. This is particularly critical for studies of transcription factor expression, or the inference and study of regulatory networks.