Evaluation of qPCR curve analysis methods for reliable biomarker discovery: Bias, resolution, precision, and implications

Ruijter JM, Pfaffl MW, Zhao S, Spiess AN, Boggy G, Blom J, Rutledge RG, Sisti D, Lievens A, De Preter K, Derveaux S, et al. (2012)
Methods. A companion to methods in enzymology (San Diego, Calif.) 59(1): 32-46.

Journal Article | Published | English

No fulltext has been uploaded

Author
; ; ; ; ; ; ; ; ; ; ;
All
Abstract
RNA transcripts such as mRNA or microRNA are frequently used as biomarkers to determine disease state or response to therapy. Reverse transcription (RT) in combination with quantitative PCR (qPCR) has become the method of choice to quantify small amounts of such RNA molecules. In parallel with the democratization of RT-qPCR and its increasing use in biomedical research or biomarker discovery, we witnessed a growth in the number of gene expression data analysis methods. Most of these methods are based on the principle that the position of the amplification curve with respect to the cycle-axis is a measure for the initial target quantity: the later the curve, the lower the target quantity. However, most methods differ in the mathematical algorithms used to determine this position, as well as in the way the efficiency of the PCR reaction (the fold increase of product per cycle) is determined and applied in the calculations. Moreover, there is dispute about whether the PCR efficiency is constant or continuously decreasing. Together this has lead to the development of different methods to analyze amplification curves. In published comparisons of these methods, available algorithms were typically applied in a restricted or outdated way, which does not do them justice. Therefore, we aimed at development of a framework for robust and unbiased assessment of curve analysis performance whereby various publicly available curve analysis methods were thoroughly compared using a previously published large clinical data set (Vermeulen et al., 2009) [11]. The original developers of these methods applied their algorithms and are co-author on this study. We assessed the curve analysis methods' impact on transcriptional biomarker identification in terms of expression level, statistical significance, and patient-classification accuracy. The concentration series per gene, together with data sets from unpublished technical performance experiments, were analyzed in order to assess the algorithms' precision, bias, and resolution. While large differences exist between methods when considering the technical performance experiments, most methods perform relatively well on the biomarker data. The data and the analysis results per method are made available to serve as benchmark for further development and evaluation of qPCR curve analysis methods (http://qPCRDataMethods.hfrc.nl).
Publishing Year
ISSN
PUB-ID

Cite this

Ruijter JM, Pfaffl MW, Zhao S, et al. Evaluation of qPCR curve analysis methods for reliable biomarker discovery: Bias, resolution, precision, and implications. Methods. A companion to methods in enzymology (San Diego, Calif.). 2012;59(1):32-46.
Ruijter, J. M., Pfaffl, M. W., Zhao, S., Spiess, A. N., Boggy, G., Blom, J., Rutledge, R. G., et al. (2012). Evaluation of qPCR curve analysis methods for reliable biomarker discovery: Bias, resolution, precision, and implications. Methods. A companion to methods in enzymology (San Diego, Calif.), 59(1), 32-46.
Ruijter, J. M., Pfaffl, M. W., Zhao, S., Spiess, A. N., Boggy, G., Blom, J., Rutledge, R. G., Sisti, D., Lievens, A., De Preter, K., et al. (2012). Evaluation of qPCR curve analysis methods for reliable biomarker discovery: Bias, resolution, precision, and implications. Methods. A companion to methods in enzymology (San Diego, Calif.) 59, 32-46.
Ruijter, J.M., et al., 2012. Evaluation of qPCR curve analysis methods for reliable biomarker discovery: Bias, resolution, precision, and implications. Methods. A companion to methods in enzymology (San Diego, Calif.), 59(1), p 32-46.
J.M. Ruijter, et al., “Evaluation of qPCR curve analysis methods for reliable biomarker discovery: Bias, resolution, precision, and implications”, Methods. A companion to methods in enzymology (San Diego, Calif.), vol. 59, 2012, pp. 32-46.
Ruijter, J.M., Pfaffl, M.W., Zhao, S., Spiess, A.N., Boggy, G., Blom, J., Rutledge, R.G., Sisti, D., Lievens, A., De Preter, K., Derveaux, S., Hellemans, J., Vandesompele, J.: Evaluation of qPCR curve analysis methods for reliable biomarker discovery: Bias, resolution, precision, and implications. Methods. A companion to methods in enzymology (San Diego, Calif.). 59, 32-46 (2012).
Ruijter, Jan M, Pfaffl, Michael W, Zhao, Sheng, Spiess, Andrej N, Boggy, Gregory, Blom, Jochen, Rutledge, Robert G, Sisti, Davide, Lievens, Antoon, De Preter, Katleen, Derveaux, Stefaan, Hellemans, Jan, and Vandesompele, Jo. “Evaluation of qPCR curve analysis methods for reliable biomarker discovery: Bias, resolution, precision, and implications”. Methods. A companion to methods in enzymology (San Diego, Calif.) 59.1 (2012): 32-46.
This data publication is cited in the following publications:
This publication cites the following data publications:

31 Citations in Europe PMC

Data provided by Europe PubMed Central.

Hypoxia and the pharmaceutical diclofenac influence the circadian responses of three-spined stickleback.
Prokkola JM, Nikinmaa M, Lubiana P, Kanerva M, McCairns RJ, Gotting M., Aquat. Toxicol. 158(), 2015
PMID: 25461750
A fixed-point algorithm for estimating amplification efficiency from a polymerase chain reaction dilution series.
Jones ME, Mayne GC, Wang T, Watson DI, Hussey DJ., BMC Bioinformatics 15(), 2014
PMID: 25492416
FXR-dependent reduction of hepatic steatosis in a bile salt deficient mouse model.
Kunne C, Acco A, Duijst S, de Waart DR, Paulusma CC, Gaemers I, Oude Elferink RP., Biochim. Biophys. Acta 1842(5), 2014
PMID: 24548803
MAKERGAUL: an innovative MAK2-based model and software for real-time PCR quantification.
Bultmann CA, Weiskirchen R., Clin. Biochem. 47(1-2), 2014
PMID: 24183882
Accurate and precise DNA quantification in the presence of different amplification efficiencies using an improved Cy0 method.
Guescini M, Sisti D, Rocchi MB, Panebianco R, Tibollo P, Stocchi V., PLoS ONE 8(7), 2013
PMID: 23861909

49 References

Data provided by Europe PubMed Central.

Model based analysis of real-time PCR data from DNA binding dye protocols.
Alvarez MJ, Vila-Ortiz GJ, Salibe MC, Podhajcer OL, Pitossi FJ., BMC Bioinformatics 8(), 2007
PMID: 17349040
Enhanced analysis of real-time PCR data by using a variable efficiency model: FPK-PCR.
Lievens A, Van Aelst S, Van den Bulcke M, Goetghebeur E., Nucleic Acids Res. 40(2), 2012
PMID: 22102586

Gentle, BioTechniques 31(), 2001
Standardized determination of real-time PCR efficiency from a single reaction set-up.
Tichopad A, Dilger M, Schwarz G, Pfaffl MW., Nucleic Acids Res. 31(20), 2003
PMID: 14530455
Comprehensive algorithm for quantitative real-time polymerase chain reaction.
Zhao S, Fernald RD., J. Comput. Biol. 12(8), 2005
PMID: 16241897
Experimental validation of novel and conventional approaches to quantitative real-time PCR data analysis.
Peirson SN, Butler JN, Foster RG., Nucleic Acids Res. 31(14), 2003
PMID: 12853650
Assumption-free analysis of quantitative real-time polymerase chain reaction (PCR) data.
Ramakers C, Ruijter JM, Deprez RH, Moorman AF., Neurosci. Lett. 339(1), 2003
PMID: 12618301
Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data.
Ruijter JM, Ramakers C, Hoogaars WM, Karlen Y, Bakker O, van den Hoff MJ, Moorman AF., Nucleic Acids Res. 37(6), 2009
PMID: 19237396
Validation of a quantitative method for real time PCR kinetics.
Liu W, Saint DA., Biochem. Biophys. Res. Commun. 294(2), 2002
PMID: 12051718
A standard curve based method for relative real time PCR data processing.
Larionov A, Krause A, Miller W., BMC Bioinformatics 6(), 2005
PMID: 15780134
A new real-time PCR method to overcome significant quantitative inaccuracy due to slight amplification inhibition.
Guescini M, Sisti D, Rocchi MB, Stocchi L, Stocchi V., BMC Bioinformatics 9(), 2008
PMID: 18667053
UNAFold: software for nucleic acid folding and hybridization.
Markham NR, Zuker M., Methods Mol. Biol. 453(), 2008
PMID: 18712296
Measurable impact of RNA quality on gene expression results from quantitative PCR.
Vermeulen J, De Preter K, Lefever S, Nuytens J, De Vloed F, Derveaux S, Hellemans J, Speleman F, Vandesompele J., Nucleic Acids Res. 39(9), 2011
PMID: 21317187

Conover, 1980
Design and optimization of reverse-transcription quantitative PCR experiments.
Tichopad A, Kitchen R, Riedmaier I, Becker C, Stahlberg A, Kubista M., Clin. Chem. 55(10), 2009
PMID: 19643838
Shape based kinetic outlier detection in real-time PCR.
Sisti D, Guescini M, Rocchi MB, Tibollo P, D'Atri M, Stocchi V., BMC Bioinformatics 11(), 2010
PMID: 20385019

Export

0 Marked Publications

Open Data PUB

Web of Science

View record in Web of Science®

Sources

PMID: 22975077
PubMed | Europe PMC

Search this title in

Google Scholar