194
views
0
recommends
+1 Recommend
0 collections
    7
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Despite the central role of quantitative PCR (qPCR) in the quantification of mRNA transcripts, most analyses of qPCR data are still delegated to the software that comes with the qPCR apparatus. This is especially true for the handling of the fluorescence baseline. This article shows that baseline estimation errors are directly reflected in the observed PCR efficiency values and are thus propagated exponentially in the estimated starting concentrations as well as ‘fold-difference’ results. Because of the unknown origin and kinetics of the baseline fluorescence, the fluorescence values monitored in the initial cycles of the PCR reaction cannot be used to estimate a useful baseline value. An algorithm that estimates the baseline by reconstructing the log-linear phase downward from the early plateau phase of the PCR reaction was developed and shown to lead to very reproducible PCR efficiency values. PCR efficiency values were determined per sample by fitting a regression line to a subset of data points in the log-linear phase. The variability, as well as the bias, in qPCR results was significantly reduced when the mean of these PCR efficiencies per amplicon was used in the calculation of an estimate of the starting concentration per sample.

          Related collections

          Most cited references27

          • Record: found
          • Abstract: found
          • Article: not found

          Comprehensive algorithm for quantitative real-time polymerase chain reaction.

          Quantitative real-time polymerase chain reactions (qRT-PCR) have become the method of choice for rapid, sensitive, quantitative comparison of RNA transcript abundance. Useful data from this method depend on fitting data to theoretical curves that allow computation of mRNA levels. Calculating accurate mRNA levels requires important parameters such as reaction efficiency and the fractional cycle number at threshold (CT) to be used; however, many algorithms currently in use estimate these important parameters. Here we describe an objective method for quantifying qRT-PCR results using calculations based on the kinetics of individual PCR reactions without the need of the standard curve, independent of any assumptions or subjective judgments which allow direct calculation of efficiency and CT. We use a four-parameter logistic model to fit the raw fluorescence data as a function of PCR cycles to identify the exponential phase of the reaction. Next, we use a three-parameter simple exponent model to fit the exponential phase using an iterative nonlinear regression algorithm. Within the exponential portion of the curve, our technique automatically identifies candidate regression values using the P-value of regression and then uses a weighted average to compute a final efficiency for quantification. For CT determination, we chose the first positive second derivative maximum from the logistic model. This algorithm provides an objective and noise-resistant method for quantification of qRT-PCR results that is independent of the specific equipment used to perform PCR reactions.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Quantitative real-time RT-PCR data analysis: current concepts and the novel "gene expression's CT difference" formula.

            For quantification of gene-specific mRNA, quantitative real-time RT-PCR has become one of the most frequently used methods over the last few years. This article focuses on the issue of real-time PCR data analysis and its mathematical background, offering a general concept for efficient, fast and precise data analysis superior to the commonly used comparative CT (DeltaDeltaCT) and the standard curve method, as it considers individual amplification efficiencies for every PCR. This concept is based on a novel formula for the calculation of relative gene expression ratios, termed GED (Gene Expression's CT Difference) formula. Prerequisites for this formula, such as real-time PCR kinetics, the concept of PCR efficiency and its determination, are discussed. Additionally, this article offers some technical considerations and information on statistical analysis of real-time PCR data.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Experimental validation of novel and conventional approaches to quantitative real-time PCR data analysis.

              Real-time PCR is being used increasingly as the method of choice for mRNA quantification, allowing rapid analysis of gene expression from low quantities of starting template. Despite a wide range of approaches, the same principles underlie all data analysis, with standard approaches broadly classified as either absolute or relative. In this study we use a variety of absolute and relative approaches of data analysis to investigate nocturnal c-fos expression in wild-type and retinally degenerate mice. In addition, we apply a simple algorithm to calculate the amplification efficiency of every sample from its amplification profile. We confirm that nocturnal c-fos expression in the rodent eye originates from the photoreceptor layer, with around a 5-fold reduction in nocturnal c-fos expression in mice lacking rods and cones. Furthermore, we illustrate that differences in the results obtained from absolute and relative approaches are underpinned by differences in the calculated PCR efficiency. By calculating the amplification efficiency from the samples under analysis, comparable results may be obtained without the need for standard curves. We have automated this method to provide a means of streamlining the real-time PCR process, enabling analysis of experimental samples based upon their own reaction kinetics rather than those of artificial standards.
                Bookmark

                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                April 2009
                22 February 2009
                22 February 2009
                : 37
                : 6
                : e45
                Affiliations
                1Heart Failure Research Center, Academic Medical Center, University of Amsterdam, The Netherlands, 2Department of Neuroscience, Faculty of Mental Health, University of Maastricht, The Netherlands, 3Nestec Ltd, PTC Orbe, Switzerland and 4Department of Endocrinology and Metabolism, Academic Medical Center, University of Amsterdam, The Netherlands
                Author notes
                *To whom correspondence should be addressed. Tel: +30 20 5665386; Fax: +30 20 6976177; Email: j.m.ruijter@ 123456amc.uva.nl

                Present addresses: C. Ramakers, Department of Clinical Chemistry & Hematology, St Elisabeth Hospital, Tilburg, The Netherlands W. M. H. Hoogaars, Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands

                Article
                gkp045
                10.1093/nar/gkp045
                2665230
                19237396
                f1fb27a1-afba-4dd9-bc1e-c8ce8334e987
                © 2009 The Author(s)

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

                History
                : 6 August 2008
                : 15 January 2009
                : 15 January 2009
                Categories
                Methods Online

                Genetics
                Genetics

                Comments

                Comment on this article