Algorithms for automated detection of hook effect-bearing amplification curves
Standard
Algorithms for automated detection of hook effect-bearing amplification curves. / Burdukiewicz, Michał; Spiess, Andrej-Nikolai; Blagodatskikh, Konstantin A; Lehmann, Werner; Schierack, Peter; Rödiger, Stefan.
in: Biomol Detect Quantif, Jahrgang 16, Nr. 16, 12.2018, S. 1-4.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - JOUR
T1 - Algorithms for automated detection of hook effect-bearing amplification curves
AU - Burdukiewicz, Michał
AU - Spiess, Andrej-Nikolai
AU - Blagodatskikh, Konstantin A
AU - Lehmann, Werner
AU - Schierack, Peter
AU - Rödiger, Stefan
PY - 2018/12
Y1 - 2018/12
N2 - 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.
AB - 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.
KW - Journal Article
U2 - 10.1016/j.bdq.2018.08.001
DO - 10.1016/j.bdq.2018.08.001
M3 - SCORING: Journal article
C2 - 30560061
VL - 16
SP - 1
EP - 4
JO - Biomol Detect Quantif
JF - Biomol Detect Quantif
SN - 2214-7535
IS - 16
ER -