A standard curve based method for relative real time PCR data processing

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Abstract

Background

Currently real time PCR is the most precise method by which to measure gene expression. The method generates a large amount of raw numerical data and processing may notably influence final results. The data processing is based either on standard curves or on PCR efficiency assessment. At the moment, the PCR efficiency approach is preferred in relative PCR whilst the standard curve is often used for absolute PCR. However, there are no barriers to employ standard curves for relative PCR. This article provides an implementation of the standard curve method and discusses its advantages and limitations in relative real time PCR.

Results

We designed a procedure for data processing in relative real time PCR. The procedure completely avoids PCR efficiency assessment, minimizes operator involvement and provides a statistical assessment of intra-assay variation.

The procedure includes the following steps. (I) Noise is filtered from raw fluorescence readings by smoothing, baseline subtraction and amplitude normalization. (II) The optimal threshold is selected automatically from regression parameters of the standard curve. (III) Crossing points (CPs) are derived directly from coordinates of points where the threshold line crosses fluorescence plots obtained after the noise filtering. (IV) The means and their variances are calculated for CPs in PCR replicas. (V) The final results are derived from the CPs' means. The CPs' variances are traced to results by the law of error propagation.

A detailed description and analysis of this data processing is provided. The limitations associated with the use of parametric statistical methods and amplitude normalization are specifically analyzed and found fit to the routine laboratory practice. Different options are discussed for aggregation of data obtained from multiple reference genes.

Conclusion

A standard curve based procedure for PCR data processing has been compiled and validated. It illustrates that standard curve design remains a reliable and simple alternative to the PCR-efficiency based calculations in relative real time PCR.

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Most cited references 21

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Processing of gene expression data generated by quantitative real-time RT-PCR.

Patrick Y Muller, Harald Janovjak, André Miserez … (2002)

Quantitative real-time PCR represents a highly sensitive and powerful technique for the quantitation of nucleic acids. It has a tremendous potential for the high-throughput analysis of gene expression in research and routine diagnostics. However, the major hurdle is not the practical performance of the experiments themselves but rather the efficient evaluation and the mathematical and statistical analysis of the enormous amount of data gained by this technology, as these functions are not included in the software provided by the manufacturers of the detection systems. In this work, we focus on the mathematical evaluation and analysis of the data generated by quantitative real-time PCR, the calculation of the final results, the propagation of experimental variation of the measured values to the final results, and the statistical analysis. We developed a Microsoft Excel-based software application coded in Visual Basic for Applications, called Q-Gene, which addresses these points. Q-Gene manages and expedites the planning, performance, and evaluation of quantitative real-time PCR experiments, as well as the mathematical and statistical analysis, storage, and graphical presentation of the data. The Q-Gene software application is a tool to cope with complex quantitative real-time PCR experiments at a high-throughput scale and considerably expedites and rationalizes the experimental setup, data analysis, and data management while ensuring highest reproducibility.

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A new quantitative method of real time reverse transcription polymerase chain reaction assay based on simulation of polymerase chain reaction kinetics.

Weihong Liu, David Saint (2002)

Real-time reverse transcription (RT) PCR is currently the most sensitive method for the detection of low-abundance mRNAs. Two relative quantitative methods have been adopted: the standard curve method and the comparative C(T) method. The latter is used when the amplification efficiency of a reference gene is equal to that of the target gene; otherwise the standard curve method is applied. Based on the simulation of kinetic process of real-time PCR, we have developed a new method for quantitation and normalization of gene transcripts. In our method, the amplification efficiency for each individual reaction is calculated from the kinetic curve, and the initial amount of gene transcript is derived and normalized. Simulation demonstrated that our method is more accurate than the comparative C(T) method and would save more time than the relative standard curve method. We have used the new method to quantify gene expression levels of nine two-pore potassium channels. The relative levels of gene expression revealed by our quantitative method were broadly consistent with those estimated by routine RT-PCR, but the results also showed that amplification efficiencies varied from gene to gene and from sample to sample. Our method provides a simple and accurate approach to quantifying gene expression level with the advantages that neither construction of standard curve nor validation experiments are needed.

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Mathematics of quantitative kinetic PCR and the application of standard curves.

Hélène C. F. Côté, R. G. Rutledge (2003)

Fluorescent monitoring of DNA amplification is the basis of real-time PCR, from which target DNA concentration can be determined from the fractional cycle at which a threshold amount of amplicon DNA is produced. Absolute quantification can be achieved using a standard curve constructed by amplifying known amounts of target DNA. In this study, the mathematics of quantitative PCR are examined in detail, from which several fundamental aspects of the threshold method and the application of standard curves are illustrated. The construction of five replicate standard curves for two pairs of nested primers was used to examine the reproducibility and degree of quantitative variation using SYBER Green I fluorescence. Based upon this analysis the application of a single, well- constructed standard curve could provide an estimated precision of +/-6-21%, depending on the number of cycles required to reach threshold. A simplified method for absolute quantification is also proposed, in which quantitative scale is determined by DNA mass at threshold.

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Author and article information

Journal

Journal ID (nlm-ta): BMC Bioinformatics

Title: BMC Bioinformatics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2105

Publication date Collection: 2005

Publication date (Electronic): 21 March 2005

Volume: 6

Page: 62

Affiliations

[1 ]Breast Unit, Western general Hospital, Edinburgh, UK

[2 ]Novartis Pharmaceuticals, Biostatistics, CH – 4002 Basel, Switzerland

[3 ]Breast Unit, Edinburgh University, Edinburgh, UK

Article

Publisher ID: 1471-2105-6-62

DOI: 10.1186/1471-2105-6-62

PMC ID: 1274258

PubMed ID: 15780134

SO-VID: be20cd5d-1f1c-4797-a067-81f86c82ee11

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 11 November 2004

Date accepted : 21 March 2005

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A standard curve based method for relative real time PCR data processing

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Most cited references 21

Processing of gene expression data generated by quantitative real-time RT-PCR.

A new quantitative method of real time reverse transcription polymerase chain reaction assay based on simulation of polymerase chain reaction kinetics.

Mathematics of quantitative kinetic PCR and the application of standard curves.

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