A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Relative quantification <s> Real-time reverse transcription polymerase chain reaction (RT-PCR) has gained wide popularity as a sensitive and reliable technique for mRNA quantification. The development of new mathematical models for such quantifications has generally paid little attention to the aspect of error propagation. In this study we evaluate, both theoretically and experimentally, several recent models for relative real-time RT-PCR quantification of mRNA with respect to random error accumulation. We present error propagation expressions for the most common quantification models and discuss the influence of the various components on the total random error. Normalization against a calibrator sample to improve comparability between different runs is shown to increase the overall random error in our system. On the other hand, normalization against multiple reference genes, introduced to improve accuracy, does not increase error propagation compared to normalization against a single reference gene. Finally, we present evidence that sample-specific amplification efficiencies determined from individual amplification curves primarily increase the random error of real-time RT-PCR quantifications and should be avoided. Our data emphasize that the gain of accuracy associated with new quantification models should be validated against the corresponding loss of precision. <s> BIB001 </s> A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Relative quantification <s> Although quantitative PCR (qPCR) is becoming the method of choice for expression profiling of selected genes, accurate and straightforward processing of the raw measurements remains a major hurdle. Here we outline advanced and universally applicable models for relative quantification and inter-run calibration with proper error propagation along the entire calculation track. These models and algorithms are implemented in qBase, a free program for the management and automated analysis of qPCR data. <s> BIB002 </s> A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Relative quantification <s> BackgroundFluorescent data obtained from real-time PCR must be processed by some method of data analysis to obtain the relative quantity of target mRNA. The method chosen for data analysis can strongly influence results of the quantification.ResultsTo compare the performance of six techniques which are currently used for analysing fluorescent data in real-time PCR relative quantification, we quantified four cytokine transcripts (IL-1β, IL-6 TNF-α, and GM-CSF) in an in vivo model of colonic inflammation. Accuracy of the methods was tested by quantification on samples with known relative amounts of target mRNAs. Reproducibility of the methods was estimated by the determination of the intra-assay and inter-assay variability. Cytokine expression normalized to the expression of three reference genes (ACTB, HPRT, SDHA) was then determined using the six methods for data analysis. The best results were obtained with the relative standard curve method, comparative Ct method and with DART-PCR, LinRegPCR and Liu & Saint exponential methods when average amplification efficiency was used. The use of individual amplification efficiencies in DART-PCR, LinRegPCR and Liu & Saint exponential methods significantly impaired the results. The sigmoid curve-fitting (SCF) method produced medium performance; the results indicate that the use of appropriate type of fluorescence data and in some instances manual selection of the number of amplification cycles included in the analysis is necessary when the SCF method is applied. We also compared amplification efficiencies (E) and found that although the E values determined by different methods of analysis were not identical, all the methods were capable to identify two genes whose E values significantly differed from other genes.ConclusionOur results show that all the tested methods can provide quantitative values reflecting the amounts of measured mRNA in samples, but they differ in their accuracy and reproducibility. Selection of the appropriate method can also depend on the design of a particular experiment. The advantages and disadvantages of the methods in different applications are discussed. <s> BIB003 </s> A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Relative quantification <s> MicroRNAs (miRNAs) are an important class of gene regulators, acting on several aspects of cellular function such as differentiation, cell cycle control and stemness. These master regulators constitute an invaluable source of biomarkers, and several miRNA signatures correlating with patient diagnosis, prognosis and response to treatment have been identified. Within this exciting field of research, whole-genome RT-qPCR based miRNA profiling in combination with a global mean normalization strategy has proven to be the most sensitive and accurate approach for highthroughput miRNA profiling (Mestdagh et al., Genome Biology, 2009). In this chapter, we summarize the power of the previously described global mean normalization method in comparison to the multiple reference gene normalization method using the most stably expressed small RNA controls. In addition, we compare the original global mean method to a modified global mean normalization strategy based on the attribution of equal weight to each individual miRNA during normalization. This modified algorithm is implemented in Biogazelle’s qbasePLUS software and is presented here for the first time. <s> BIB004 </s> A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Relative quantification <s> This paper aims to discuss various aspects of the use of reference genes in qPCR technique used in the thousands of present studies. Most frequently, these are housekeeping genes and they must meet several criteria so that they can lay claim to the name. Lots of papers report that in different conditions, for different organisms and even tissues the basic assumption—the constant level of the expression is not maintained for many genes that seem to be perfect candidates. Moreover, their transcription can not be affected by experimental factors. Sounds simple and clear but a great number of designed protocols and lack of consistency among them brings confusion on how to perform experiment properly. Since during selection of the most stable normalizing gene we can not use any reference gene, different ways and algorithms for their selection were developed. Such methods, including examples of best normalizing genes in some specific cases and possible mistakes are presented based on available sources. Numerous examples of reference genes applications, which are usually in too few numbers in relevant articles not allowing to make a solid fundament for a reader, will be shown along with instructive compilations to make an evidence for presented statements and an arrangement of future qPCR experiments. To include all the pitfalls and problems associated with the normalization methods there is no way not to begin from sample preparation and its storage going through candidate gene selection, primer design and statistical analysis. This is important because numerous short reviews available cover the topic only in lesser extent at the same time giving the reader false conviction of complete topic recognition. <s> BIB005
Relative quantification is based on internal reference genes to determine fold-differences in expression of the target gene. It avoids the need for a standard curve as the amount of the studied gene is compared to the amount of a reference gene. Various mathematical models are established to calculate the expression of a target gene in relation to an adequate reference gene BIB002 . Based on the method, they can perform normalization using single or multiple reference genes, include PCR efficiency, and allow calibration across multiple plates. The final calculated relative expression level relates the target gene in a sample relative to a calibrator sample. The generated relative quantities can be compared across multiple real-time RT-PCR experiments. Appropriate normalization strategies and selection of reference genes have been reviewed elsewhere BIB003 BIB005 . For large scale expression profiling studies it has been shown that mean expression value normalization outperforms the normalization strategy based on a few reference genes BIB004  . As relative quantities are determined by computations based on several observables (e.g., replicates, amplification efficiency), it has been shown that the random error of the final result is influenced by error propagation. Consequently, methods have been developed to include error propagation into relative quantification calculations BIB001 .