Conference Agenda

qPCR-DA2: qPCR Data Analysis 2
Wednesday, 20/Mar/2019:
2:00pm - 4:00pm

Session Chair: Jan M Ruijter, Academic Medical Center, Amsterdam, Netherlands, The
Location: HS 15
ZHG -- Lecture Hall 15


Integration of DNA Melting Curve Analysis In qPCR Data Analysis

Maurice J.B. van den Hoff1, Quinn D. Gunst1, Adrian Ruiz-Villalba2, Carl Wittwer3, Jan M. Ruijter1

1Amsterdam UMC, location AMC, Depart. Medical Biology, Amsterdam, The Netherlands; 2Foundation of Applied Medical Research, University of Navarra, Pamplona, Spain; 3University of Utah Health Sciences Center, Department of Pathology, Salt Lake City, UT, USA

Quantitative PCR (qPCR) allows the precise measurement of DNA concentrations and is generally considered to be straightforward and trouble free. However, analysis of the results of 101 validated SybrGreen I-based assays for genes related to the Wnt-pathway in 5 different cardiac compartments frequently showed the amplification of nonspecific products, most probably primer-dimers. A detailed survey of these data revealed that the occurrence of nonspecific products is not related to Cq value or the PCR efficiency. qPCRs amplifying both specific and non-specific products can easily be identified when a melting curve analysis is performed. Currently, qPCRs that amplify both the specific and (a) nonspecific product(s) need to be excluded from further analysis because the quantification result is meaningless.

A model was developed, allowing the quantification of a qPCR in which the correct product together with additional off-target products is amplified. This model is based on the analysis of the melting peaks and the assignment of the total fluorescence at the end of the reaction to either the correct product or to other products. The fraction of fluorescence due to the amplification of the correct product can then be used to correct the quantification result (Cq value or target quantity, N0) that was derived from the observed amplification curve.

This correction method, and a program to analyze melting curves, was tested for the 101 different validated qPCR assays in different biological tissues and for model experiments with known concentrations of different products. The results of these tests show improvement of the sensitivity of SybrGreen I-based assays and avoid erroneous conclusion.

Fundamentals for the Automatic Classification of Quantitative PCR AmplificationCurves - A Biostatistical Approach

Stefan Rödiger1, Andrej-Nikolai Spiess2, Michał Burdukiewicz3

1Brandenburg University of Technology Cottbus - Senftenberg, Germany; 2University Medical Center Hamburg-Eppendorf, Germany; 3Warsaw University of Technology, Poland

Quantitative polymerase chain reaction (qPCR) is a widely used bioanalytical method in forensics, human diagnostics and life sciences. With this method nucleic acids are detected and quantified. In qPCRs, the enzymatic amplification of the target DNA (amplicon) is monitored in real-time by fluorescent reporter molecules marking the synthesized PCR products cycle by cycle. The measured fluorescence is proportional to the amplicon amount.
For diagnostic and forensic applications in particular, the question arises for example as to whether an amplification reaction is negative or positive. Of interest is also and automatic classification of the quality of amplification curves. Until now, such classification was usually performed manually or on the basis of fixed threshold values. However, this approach is error-prone if inadequate thresholds are used or the user performs the classification subjectively based on his experience.
Therefore, the classifications of the same sample may not be identical for different users. Such errors are problematic because they can lead to an erroneous judgement. Therefore we developmed a scientific open source software, called PCRedux ( With this software, predictors (features) of amplification curves can be calculated automatically. A predictor is a quantifiable informative property of an amplification curve. A set of statistical algorithms for the calculation of predictors os proposed. The work also shows how predictors can be used in tests and logical combinations to perform machine-based classifications.
All scientific work depends on the data, with open data in particular being regarded as a cornerstone of science. Since no data sets of classified amplification curves were available, the work also deals with the aggregation, management and distribution of classified qPCR data sets. Manual classification of amplification curves is time-consuming and error-prone, especially for large data sets. To improve this, auxiliary tools have been developed.
A open approach for curve-shape based group classification was proposed.

GEAR: The Genome Analysis Server Eases Wet-Lab Data Analysis

Tobias Rausch1, Markus Hsi-Yang Fritz2, Vladimir Benes1, Andreas Untergasser1,3

1European Molecular Biology Laboratory, Genomics Core Facility, Heidelberg, Germany; 2European Molecular Biology Laboratory, Genome Biology Unit, EMBL, Heidelberg, Germany; 3Heidelberg University, Germany

The genome analysis server (GEAR: is a wide collection of tools supporting molecular biologists in everyday lab tasks. An enhanced version of Primer3Plus allows the selection of primers for many use cases like detection, qPCR, cloning and sequencing. Secondary structures are now also drawn and can be evaluated by the researcher. Silica can perform in-silico PCRs on a selected genome with a set of provided primers. It localizes primer binding sites and calculates the amplicons. The Wily-DNA-Editor is a DNA sequence editor supporting genbank files and sufficient for common plasmid manipulation tasks. Users can edit or reverse complement the sequence, find restriction sites, draw restriction maps, calculate digests, find open reading frames, translate sequences and allows a custom feature annotation. Due to its JavaScript nature all data are processed in the user's browser without being transferred to the server. Teal, Sage and Indigo display Sanger trace files and extract the sequence information. They ease the evaluation by aligning the trace file to a genome or a provided reference sequence highlighting the found differences. Last, the RDML tools support users in the evaluation and the padding of RDML files. The user can validate the files against the schema format description, fix common errors and build RDML files from table data. Ultimately, the RDML tools will allow to edit and analyze RDML files as well as evaluating compliance with MIQE.

These tools are very useful for molecular biologists as they solve common lab tasks and enable to work at any computer with internet connection and a current browser - without the need of installing software locally. The code is open source and users that due to legal restrictions cannot send their data on servers over the internet may opt to install an own version of gear on a local server and process their data in house.

Digital PCR provides new challenges. The RDML format has to be extended to support dPCR data in an efficient way and the tools have to be extended to visualize the data. Last, we would like to draw attention to a session on RDML and digital PCR were everybody is invited to provide suggestions on the further development of RDML.

DAILYqpcr – An Application For Revolutionizing Designing, Storing, And Analyzing QPCR Experiments

Stephan Pabinger, Anna Majewski, Manuela Hofner, Walter Pulverer, Priska Bauerstätter, Stefanie Eile, Julie Krainer, Andreas Weinhäusel, Klemens Vierlinger

AIT - Austrian Institute of Technology, Austria

Quantitative real-time polymerase chain reaction (qPCR) is a standard method in most laboratories for quantification of gene expression. However, the streamlined design of experiments, its analysis, and the controlled storage of results is still an unresolved problem.

Here we present a novel tool that allows the seamless integration between lab and data analysis workflows with a strong focus on usability. DAILYqpcr is a Python and R based web-application that is centered around two main aspects: (i) an interactive designer to outline the qPCR experiment before it is processed in the laboratory; (ii) a collection of analysis workflows tailored to specific use-cases such as methylation analysis or differential gene expression.

Instead of offering a plethora of methods and tools where the user needs to know exactly how to use them, we focus on providing wizard-like analysis solutions for specific use-cases customized to the tasks and needs of the scientists. Depending on the type of experiment, the appropriate analysis tools and parameters are selected and configured for the user. This allows a streamlined experience reducing the analysis time while at the same time avoiding the misuse of methods.

As an example, the workflow assay validation starts with reading in the data from the thermocycler (currently Lightcycler and Fluidigm are supported), continues with customized quality assessment steps, and outputs performance characteristics and interactive plots about each tested assay. Throughout the workflow the user is guided through the necessary steps, each of which is stored to allow resuming the analysis at a later timepoint.

The integrated database stores data, settings and results, hence allowing researchers to search for analysis outcomes, samples, assays, designs and other resources. For example, users can check whether an assay has already been applied for a specific set of genes or if samples were already used in other experiments. Furthermore, the application incorporates widely used R-packages, provides convenient import and export mechanisms, and can be easily extended with new use-cases.

In summary, we present a novel tool that streamlines the experience of working with qPCR data and provides a novel way to design and analyze qPCR experiments.