Abstract
Progressively more qPCR assays have been developed in recent years in numerous fields of application. These assays are routinely validated using calibration curves, but essential validation per se such as Poisson analysis is frequently neglected. However, validation is crucial for determination of resolution and quantitative and qualitative limits. The new test method PCR-Stop analysis presented in this work investigates assay performance during initial qPCR cycles. PCRs with one to five pre-runs are performed while the subsequent main qPCR runs reflect pre-run replication rates. Ideally, DNA doubles according to pre-runs, there is no variation between replicates and qPCR starts immediately at the first cycle with its average efficiency. This study shows two exemplary qPCR assays, both with suitable calibration curves and efficiencies. We demonstrated thereby the benefits of PCR-Stop analysis revealing quantitative and qualitative resolution of both assays, the limits of one of those assays and thus avoiding misinterpretations in qPCR analysis. Furthermore, data displayed that a well performing assay starts indeed with its average efficiency.
Highlights
The quantitative polymerase chain reaction was first described in the 1990s1,2 and since it has become a popular method in a wide field of applications
Sufficient information of main performance parameters can be obtained by testing the quantitative polymerase chain reaction (qPCR) using Poisson analysis which reveals quantitative and qualitative resolution in the Boundary Limit Area
The test system should show whether there is equal DNA duplication during initial cycles corresponding to overall efficiency as displayed in the calibration curve (Ct-Method)
Summary
The quantitative polymerase chain reaction (qPCR) was first described in the 1990s1,2 and since it has become a popular method in a wide field of applications. Efficiency (and Rsq) reflects only a small statistical sample (the standard DNA samples) This method provides limited main performance parameters information. Validations based on Poisson distribution have steadily grown in popularity and applicability over recent years[10,11,12] It operates within the range 1 to
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