DNA qPCR quantification

qPCR Data Analysis Made Easy

Despite the advancement of new molecular biology techniques like ddPCR or  RNAseq, quantitative PCR (qPCR) is still a golden standard for the quantification of nucleic acids, with applications in diagnostics and research. The method is based on real-time monitoring of amplification of the target DNA allowing its quantification (see basics). Yet, for the method to be quantitative, it has to be done properly. The method involves several steps, each of them producing errors, therefore, in order to get reliable results, we have to control all the steps as proposed by the MIQE standards. Our previous work on GMO quantification has identified several factors, critical for the qPCR-based quantification of the nucleic acids.

It’s all about efficiency

Like with everything else in life, the efficiency is crucial. And so it is with qPCR.  The efficiency of PCR amplification is defined as the fraction of target molecules that are copied in one PCR cycle. It is considered as one of the most important parameters, strongly influencing the final result. Deviations from an optimal 100% efficiency are observed as inhibition, caused by the presence of inhibitory components, or over-amplification, caused by a compound or structural conformation changes during the PCR (see one of our qPCR tips for details). Most commonly, we are talking about efficiency in the context of the assay (i.e. a set of primers and probe, used for quantification), yet it is not uncommon that the same amplicon is amplified with different efficiency in different samples. This, so-called individual sample efficiency can therefore drastically influence the final result. Ultimately, the samples with inappropriate efficiencies cannot be properly quantified and should be removed from quantification. But how do we know that a sample is behaving badly? Obviously, making a full serial dilution curve for each sample is unfeasible, but previous research showed that performing two serial dilutions from the same sample can detect such outliers.

Another quality control parameter? How can we use it?

There are several commercial or open source software available for the analysis of qPCR data (reviewed here). Of course, most of them are dealing with efficiency. They will warn you about efficiency, enable efficiency calculation for an assay, and even enable efficiency correction by recalculating your sample data based on the assay efficiency. But what about individual sample efficiency? So far, it was not implemented in any of the software solutions. We tried to do it in Excel, but you might know what a nightmare it is: formulas falling apart, gene names becoming dates… That’s why we have developed our own web application, quantGenius that is controlling individual sample efficiency by comparing the results from two serial dilutions of the same sample. If a sample with poor efficiency is detected, the result for this sample is not calculated, preventing false interpretation of the result. In our study, we have shown the importance of using this quality control parameter in two different case studies, a plant gene expression study and a GMO detection example.

 quantGenius web application

However, dealing with individual sample efficiency is just one of the features of the quantGenius. It is designed as a simple and intuitive tool that offers full flexibility for different experimental setups. It helps you trough the whole process of data analysis: your data is first imported, interactively calculated and then exported (see image below). It is based on standard curve quantification using one or more reference genes for normalization.  The application will visually alert you of various issues like pipetting errors, bad sample efficiency etc.

quantGenious qpcr workflow

The application uses all the available qPCR-based quantification knowledge and standards and implements it in a quality control-based decision support system that will, depending on how strict you want to be, make changes in the final results. For this, it uses a hierarchically organized decision three which ensures that only high-quality data is used for biological interpretation.

Now you see it, now you don’t

One of the included quality control parameters is control of nucleic acid extraction. In typical settings, problems with nucleic acid extraction or reverse transcription are reflected in lower than usual quantities of the reference gene which should be proportional to the amount of biological material. In quantGenius, the user can therefore set a cutoff value for the reference gene which guarantees that the sample was extracted and transcribed OK.

Other buzzwords for the nucleic acid quantification are LOD (level of detection) and LOQ (level of quantification). Obviously, you get no result for the targets that are present below LOD in your samples. On the other hand, it is possible to get a result for the target that is below LOQ, but this result is plain wrong!  quantGenius will detect the samples that are under LOQ by a user-defined threshold or by technical replicate data variability, which greatly increases under LOQ. Nevertheless, when you want to show big differences in the amount of target DNA between the sample groups, losing this data will make your fold change calculation and statistical testing impossible. That’s why quantGenius will impute these with appropriately lower values and flagging them so you will distinguish the imputed from the detected values.

Showing the significance

The export from quant genius is a simple gene-sample data matrix, that can be used to prepare nice graphs and perform statistical analysis.

Sounds easy? Give quantGenius a try and tell what you think! Want to know more? Read our paper in BMC Bioinformatics, or the manual on the quantGenius website.


Špela Baebler, PhD, Scientific Associate at the National Institute of Biology, Slovenia



Baebler,Š., Svalina,M., Petek,M., Stare,K., Rotter,A., Pompe-Novak,M. and Gruden,K. (2017) quantGenius: implementation of a decision support system for qPCR-based gene quantification. BMC Bioinformatics, 18, 276.

Cankar,K., Stebih,D., Dreo,T., Zel,J. and Gruden,K. (2006) Critical points of DNA quantification by real-time PCR–effects of DNA extraction method and sample matrix on quantification of genetically modified organisms. BMC Biotechnol, 6, 37.

Bustin,S.A., Benes,V., Garson,J.A., Hellemans,J., Huggett,J., Kubista,M., Mueller,R., Nolan,T., Pfaffl,M.W., Shipley,G.L., et al. (2009) The MIQE guidelines:Minimum Information for publication of quantitative real-time PCR experiments. Clin Chem, 55, 611–622.

Pabinger,S., Rödiger,S., Kriegner,A., Vierlinger,K. and Weinhäusel,A. (2014) A survey of tools for the analysis of quantitative PCR (qPCR) data. Biomol Detect Quantif, 1, 23–33.


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