Abstract

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. 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. Autocorrelation analysis reveals periodicities of 12 for 96-well systems and 24 for a 384-well system, indicating a correlation with block architecture. Passive dye experiments show that the effect may be from optical detector bias. Importantly, the signal periodicity manifests as periodicity in quantification cycle (Cq) 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. Accordingly, any scale variability in the growth curves will lead to bias in constant-threshold-based Cqs, making it mandatory that workers should either use scale-insensitive Cqs or normalize their growth curves to constant amplitude before applying the constant threshold method.

Highlights

  • Real-time quantitative polymerase chain reaction data are found to display periodic patterns in the fluorescence intensity as a function of sample number for fixed cycle number

  • The mechanistic models avoid the calculation of E and Cq in directly estimating F09–11, but they do so by tacitly setting E = 2 throughout the baseline region[12]

  • We investigated the effect of periodic fluorescence on the estimation of threshold- (Ct) and SDM (CqSDM)-based Cq values for the ‘380-replicates’ dataset

Read more

Summary

Introduction

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. The goal of these algorithms is the estimation of initial template fluorescence (F0) and copy number (N0) Most methods accomplish this in one of two ways: by estimating the amplification efficiency E as well as quantification cycle Cq and applying these two estimates to the basic exponential growth equation, or by fitting mechanistic models of PCR kinetics. In the former approaches, E is assumed constant up to Cq and is estimated by calibration curve analysis[3] or from single curve fitting[4,5,6,7,8].

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call