Abstract
BackgroundCurrently 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.ResultsWe 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.ConclusionA 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.
Highlights
Real time PCR is the most precise method by which to measure gene expression
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
Crossing points calculation The crossing points (CPs) were calculated directly as the coordinates of points in which the threshold line crossed the broken lines representing fluorescence plots obtained after the noise filtering (Figure 2)
Summary
Real time PCR is the most precise method by which to measure gene expression. This article provides an implementation of the standard curve method and discusses its advantages and limitations in relative real time PCR. The basic choice in relative real time PCR calculations is between standard curve and PCR-efficiency based methods. The PCR efficiency approach appears as the method of choice in data processing for relative PCR [12]. Background subtraction is a common step in PCR data processing Often it requires operator's involvement to choose between several available options (e.g. subtraction of minimal value through the run, subtraction of average over a certain cycle ranges, different kinds of "trends", etc). To avoid the operator involvement we always subtract the minimal value observed in the run This option has a clear interpretation and works well. The noise potentially affecting minimal values has already been reduced before baseline subtraction
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