The Library
The impact of quantitative optimization of hybridization conditions on gene expression analysis
Tools
Sykacek, Peter, Kreil, David, Meadows, Lisa A, Auburn, Richard P, Fischer, Bettina, Russell, Steven and Micklem, Gos (2011) The impact of quantitative optimization of hybridization conditions on gene expression analysis. BMC Bioinformatics, Vol.12 (No.1). p. 73. doi:10.1186/1471-2105-12-73 ISSN 1471-2105.
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: http://dx.doi.org/10.1186/1471-2105-12-73
Abstract
Background
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.
Results
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.
Conclusions
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.
Item Type: | Journal Article | ||||
---|---|---|---|---|---|
Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) | ||||
Journal or Publication Title: | BMC Bioinformatics | ||||
Publisher: | BioMed Central Ltd. | ||||
ISSN: | 1471-2105 | ||||
Official Date: | 14 March 2011 | ||||
Dates: |
|
||||
Volume: | Vol.12 | ||||
Number: | No.1 | ||||
Page Range: | p. 73 | ||||
DOI: | 10.1186/1471-2105-12-73 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |