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Therefore, it is important to correct for background fluorescence variations that cause differences in the baseline ( Figure 10.1).Ī common approach is to use the fluorescence intensity during early cycles, such as between cycles 5 to15, to identify a constant and linear component of the background fluorescence. However, variation in background signal may hinder quantitative comparison of different samples. In well-designed assays, the background is low when compared to the amplified signal.
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The background fluorescence may be caused by a range of factors, which include choice of plasticware, remaining probe fluorescence that is not quenched, light leaking into the sample well, and differences in the optical detection for a given microtiter plate well. However, qPCR measurements that are based on amplification curves are sensitive to background fluorescence. It is beyond the scope of this guide to delve into the fine details of all of these algorithms. Different analysis packages that are associated with different instruments, have alternative approaches for determining the C q (and also use alternative names, e.g., C t, C p, take off point). Deriving Accurate C q Values Baseline CorrectionĪ C q value is determined for each target in each sample. The process of deriving and analyzing those C q values to provide reliable data that represent the biological story is presented in this chapter. The result of these processes is the generation of a set of C q values for each target in each sample. Each of these factors should be optimized to result in an assay that provides the closest possible value to the actual quantity of gene (target) in the reaction. Throughout this guide, the factors that contribute to variations in the measurement of nucleic acid using PCR or qPCR have been highlighted. In some cases, it may be possible to analyze end-point data to make a semi-quantitative analysis of the PCR yield, but quantitative measurements are more often made using qPCR and analysis of quantification cycle values (C q) 1 values. In each case, endpoint data provides a qualitative analysis after the PCR has reached plateau phase. For some applications, a qPCR will be run with the end-point data used for analysis, such as for SNP genotyping. After a traditional PCR has been completed, the data are analyzed by resolution through an agarose gel or, more recently, through a capillary electrophoresis system.