Amplification curves from quantitative Real-Time PCR experiments typically exhibit a sigmoidal shape. They can roughly be divided into a ground or baseline phase, an exponential amplification phase, a linear phase and finally a plateau phase, where in the latter, the PCR product concentration no longer increases. Nevertheless, in some cases the plateau phase displays a negative trend, e.g. in hydrolysis probe assays. This cycle-to-cycle fluorescence decrease is commonly referred to in the literature as the hook effect. Other detection chemistries also exhibit this negative trend, however the underlying molecular mechanisms are different. In this study we present two approaches to automatically detect hook effect-like curvatures based on linear (hookreg) and nonlinear regression (hookregNL). As the hook effect is typical for qPCR data, both algorithms can be employed for the automated identification of regular structured qPCR curves. Therefore, our algorithms streamline quality control, but can also be used for assay optimization or machine learning.
