Lunchtime Workshop
13:00-13:45, 17 September 2008
Real-Time qPCR data analysis: Where not to fail
Presented by Ramon Goni, Integromics’ qPCR specialist (Madrid)
Booth # 8
Several years after its discovery, thousands of high-impact studies have been conducted and published using Real-Time quantitative PCR (RT-qPCR) technique (1-3). Given its high sensitivity, RT-qPCR is the preferred method for microarray data validation (4); however the most exciting applications have been in the discovery of new biomarkers as well as in diagnosis prediction (5). Despite the fact that this technique has been widely used by researchers, there are several obstacles from designing an experiment to bottlenecks in analyzing the vast amounts of data generated.
Following some fundamental rules, data analysis is performed first by generating a hypothesis, then by designing the experiment, followed by analysis with the use of an appropriate statistical test to examine the significance of the results. An experimental design is essential to the success of a study and must be done prior to sample preparation. The experimental design is followed by several quality control inspections of the raw data to detect gene and sample outliers, filtering of non-expressed genes which when compared may provide false information with regard to fold change as well as selection of optimal endogenous controls for normalization to improve the accuracy of comparing the different samples.
A primary goal is to improve the process of scientific experiments, making data analysis explicit by providing a workflow and minimizing the discretional criteria, and input of humans in the process to only when it is required. Thus, scientific data analysis workflows are important to form a structured process which will allow obtaining reliable and reproducible results. The workshop will review the entire workflow process of data analysis and how this is achieved using Real-Time StatMiner™ (6).
1. Heid, C.A., J. Stevens, K.J. Livak, and P.M. Williams. 1996. Real time quantitative PCR. Genome Res. 6:986-994.
2. Higuchi, R., C. Fockler, G. Dollinger, and R. Watson. 1993. Kinetic PCR analysis: real-time monitoring of DNA amplification reactions. Biotechnology (N. Y.) 11:1026-1030.
3. Heather D. VanGuilder, Kent E. Vrana, and Willard M. Freeman 2008 Twenty-five years of quantitative PCR for gene expression analysis BioTechniques 25th Anniversary Issue: pp S619-S626
4. Canales, R.D., Y. Luo, J.C. Willey, B. Austermiller, C.C. Barbacioru, C. Boysen, K. Hunkapiller, R.V. Jensen, et al. 2006. Evaluation of DNA microarray results with quantitative gene expression platforms. Nat. Biotechnol. 24:1115-1122.
5. Gillis AJ, Stoop HJ, Hersmus R, Oosterhuis JW, Sun Y, Chen C, Guenther S, Sherlock J, Veltman I, Baeten J, van der Spek PJ, de Alarcon P, Looijenga LH. 2007. High-throughput microRNAome analysis in human germ cell tumours. J Pathol. 213:319-28.
6. A qPCR data analysis package available for trial access on http://www.integromics.com/StatMiner.php