Stefaan Derveaux1,2, Jo Vandesompele1,2, Jan Hellemans1,2
1 Center for Medical Genetics Ghent, Ghent University Hospital, Belgium; 2 Biogazelle, Ghent, Belgium
The PCR amplification efficiency plays a critical role for accurate and reliable qPCR quantification. Serial dilution series of a mixture of samples representative of the whole set of samples in the experiment are currently considered as the gold standard to determine the efficiency of an assay. The use of such a standard curve requires a substantial amount of time and resources and relies on the assumption that all samples are amplified with the same efficiency, which may not always be true. To address these bottlenecks, various algorithms have been proposed to determine the efficiency of an individual qPCR reaction based on the raw fluorescent data curve. In this study, we evaluated two of those single curve efficiency determination algorithms, LinRegPCR and PCR Miner, and developed a framework to compare their accuracy and precision with the gold standard used serial dilution series. By using uniformity plates (plates with hundreds of replicates of the same reaction) we were able to determine the precision of the algorithms and by using different ratios of primers and competimers (P/C; C= non-functional primers that compete with the functional primers) in a reaction, we were able to modulate the amplification efficiency in a controlled manner. Both algorithms are often not accurate in that calculated efficiencies deviate from the gold standard. While the difference in efficiency varied between assays, both algorithms measured the same (relative) difference in efficiency between reactions with different P/C ratios as determined by the gold standard. The precision is relatively low, but similar to the one of a standard curve (obtained by iteratively calculating the efficiency using only one randomly selected replicate per dilution). As such, the single curve efficiency algorithms are possibly able to identify bad reactions which amplification efficiencies that greatly differ from that of the average sample population. This has great potential for sample and run quality control.
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