Data-driven normalization strategies for high-throughput quantitative RT-PCR

Mar, Jessica C., Kimura, Yasumasa, Schroder, Kate, Irvine, Katherine M., Hayashizaki, Yoshihide, Suzuki, Harukazu, Hume, David and Quackenbush, John (2009) Data-driven normalization strategies for high-throughput quantitative RT-PCR. BMC Bioinformatics, 10 110.1-110.10. doi:10.1186/1471-2105-10-110


Author Mar, Jessica C.
Kimura, Yasumasa
Schroder, Kate
Irvine, Katherine M.
Hayashizaki, Yoshihide
Suzuki, Harukazu
Hume, David
Quackenbush, John
Title Data-driven normalization strategies for high-throughput quantitative RT-PCR
Journal name BMC Bioinformatics   Check publisher's open access policy
ISSN 1471-2105
Publication date 2009-04-19
Year available 2009
Sub-type Article (original research)
DOI 10.1186/1471-2105-10-110
Open Access Status DOI
Volume 10
Start page 110.1
End page 110.10
Total pages 11
Editor Dr. Melissa Norton
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
Subject C1
970106 Expanding Knowledge in the Biological Sciences
060102 Bioinformatics
Abstract Background: High-throughput real-time quantitative reverse transcriptase polymerase chain reaction (qPCR) is a widely used technique in experiments where expression patterns of genes are to be profiled. Current stage technology allows the acquisition of profiles for a moderate number of genes (50 to a few thousand), and this number continues to grow. The use of appropriate normalization algorithms for qPCR-based data is therefore a highly important aspect of the data preprocessing pipeline.
Formatted abstract
Background High-throughput real-time quantitative reverse transcriptase polymerase chain reaction (qPCR) is a widely used technique in experiments where expression patterns of genes are to be profiled. Current stage technology allows the acquisition of profiles for a moderate number of genes (50 to a few thousand), and this number continues to grow. The use of appropriate normalization algorithms for qPCR-based data is therefore a highly important aspect of the data preprocessing pipeline.

Results
We present and evaluate two data-driven normalization methods that directly correct for technical variation and represent robust alternatives to standard housekeeping gene-based approaches. We evaluated the performance of these methods against a single gene housekeeping gene method and our results suggest that quantile normalization performs best. These methods are implemented in freely-available software as an R package qpcrNorm distributed through the Bioconductor project.

Conclusion
The utility of the approaches that we describe can be demonstrated most clearly in situations where standard housekeeping genes are regulated by some experimental condition. For large qPCR-based data sets, our approaches represent robust, data-driven strategies for normalization.
Keyword Real-time
qPCR
Normalization methods
polymerase chain reaction
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID 1P50 HG004233
Institutional Status UQ
Additional Notes Article number 110

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2010 Higher Education Research Data Collection
ERA 2012 Admin Only
Institute for Molecular Bioscience - Publications
 
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Created: Thu, 03 Sep 2009, 18:03:13 EST by Mr Andrew Martlew on behalf of Institute for Molecular Bioscience