To date, a plethora of algorithms has been developed for absolute and relative quantitation of qPCR data. In addition to an accurate experimental design and a sensitive workflow 2, qPCR data analysis constitutes a crucial step. Quantitative real-time polymerase chain reaction (qPCR) is the most widely applied molecular biology laboratory technique for gene expression analysis 1. Accordingly, any scale variability in the growth curves will lead to bias in constant-threshold-based C qs, making it mandatory that workers should either use scale-insensitive C qs or normalize their growth curves to constant amplitude before applying the constant threshold method. Importantly, the signal periodicity manifests as periodicity in quantification cycle ( C q) values when these are estimated by the widely applied fixed threshold approach, but not when scale-insensitive markers like first- and second-derivative maxima are used. Passive dye experiments show that the effect may be from optical detector bias. Autocorrelation analysis reveals periodicities of 12 for 96-well systems and 24 for a 384-well system, indicating a correlation with block architecture. This behavior is seen for technical replicate datasets recorded on several different commercial instruments it occurs in the baseline region and typically increases with increasing cycle number in the growth and plateau regions. Real-time quantitative polymerase chain reaction (qPCR) data are found to display periodic patterns in the fluorescence intensity as a function of sample number for fixed cycle number.
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