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.

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

Andreas Untergasser
Tobias Rausch1, Markus Hsi-Yang Fritz2, Vladimir Benes1, Andreas Untergasser1,3
1) European Molecular Biology Laboratory, Genomics Core Facility, Heidelberg, Germany;
2) European Molecular Biology Laboratory, Genome Biology Unit, EMBL, Heidelberg, Germany;
3) Heidelberg University, Heidelberg, Germany;

The genome analysis server (GEAR: https://gear.embl.de) 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.

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

Stefan Rödiger1, Andrej-Nikolai Spiess2, Michał Burdukiewicz3
1) Brandenburg University of Technology Cottbus – Senftenberg, Germany;
2) University Medical Center Hamburg-Eppendorf, Germany;
3) Warsaw 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 (https://cran.r-project.org/package=pcr). 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.

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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
1) Amsterdam UMC, location AMC, Depart. Medical Biology, Amsterdam, The Netherlands;
2) Foundation of Applied Medical Research, University of Navarra, Pamplona, Spain;
3) University 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.

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Why reporting Cq or delta-Cq is senseless

Jan M Ruijter
Academic Medical Center, Amsterdam, the Netherlands, The Netherlands

With the introduction of quantitative PCR (qPCR) it was assumed that the amplification efficiency, the fold-increase per cycle, was always close to 2. This simplification allowed the use of the so-called comparative Cq equation to calculate the fold-difference between target and reference genes in treated and control tissues. Over the years the original equation (2-ΔΔCq) seems to have lost its base and the minus sign. The remainder became so ingrained in qPCR-based papers that ‘ddCq’ currently seems to be the unit in which qPCR data are measured and have to be reported. However, the variations in annotation of the figure axes make that the presented data often cannot be interpreted.
The Cq value is defined by the general principle that the position of the amplification curve with respect to the cycle-axis, reflected in the Cq value, is a measure for the initial target quantity: the ‘later’ the curve, the higher the Cq value and the lower the starting quantity of the target-of-interest. However, this position is also dependent on the amplification efficiency. Therefore, reporting only ddCq implicitly accepts unvalidated assumptions about the amplification efficiencies involved. Reported Cq values can only be interpreted with the simplifying, and false, assumption that every PCR assay in the experiment is 100% efficient. Because of this assumption, the interpretation of Cq values always leads to an unknown bias.
The bias that is introduced by ignoring the actual PCR efficiency of target and reference genes can be prevented with the calculation of the so-called efficiency-corrected target quantities or fold-differences. This was already proposed in the early years of this millennium and is recommended in the MIQE guidelines. Indeed, such efficiency-corrected target quantities are reported by a number of qPCR data analysis methods published over a decennium ago. However, this need for efficiency-correction of qPCR results is still largely ignored by researchers, reviewers and publishers. This common shortcoming of the PCR research community may be the main reason for the limited reproducibility of reported qPCR results.

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GenEx – The Ultimate Software for Analysis of Transcriptomic Data

Mikael Kubista, Amin Forootan
Amin Forootan1, Björn Sjögreen1, Mikael Kubista2
1) Multid Analyses AB, Sweden;
2) TATAA Biocenter, Sweden

With the emergence of RNA sequencing (RNASeq) transcriptome profiling entered a new era. High throughput high quality whole transcriptome data can today be collected routinely. The challenge is no longer acquiring data but rather analyzing and interpreting them. Analysis includes validating data quality, merging runs, normalizing the data, comparing experimental conditions, testing hypothesis and interpreting the results. GenEx is the most used software for qPCR data analysis and with the launch of GenEx 7, here at the 9th Gene Quantification Event, also RNASeq data can readily be analyzed. GenEx is developed for experimentalists, with a user-friendly intuitive interface that provides a smooth analytical workflow for statistical analyses of the data in compliance with guidelines when relevant. Very large data sets, typical of RNASeq, are easily and rapidly handled and graphical interfaces allow interactive analyses with powerful methods such as DESeq2, and Normfinder for normalization, t-test, Mann-Whitney, Wilcoxon’s test and ANOVA models for group comparisons, hierarchical clustering, self-organizing maps (SOM) and principal component analysis (PCA) for clustering, dynamic PCA with statistical filters for variable selection to find the most relevant expression markers, kinetic PCA for time studies, survival analysis to compare treatments, and artificial neural network (ANN) and support vector machines (SVM) to build predictive models. GenEx 7 is continuously updated to include new methods and strategies as they become available and to maintain compatibility with qPCR and NGS instrument software, computer operating systems, and graphical and printer routines. GenEx 7 is the only data analysis software supported by the majority of leading instrument and solution providers.
Download the latest GenEx version here.

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GeneGini: Assessment via the Gini Coefficient of Reference “Housekeeping” Genes and Diverse Human Transporter Expression Profiles

Philip Day1, Stephen O’Hagan1, Marina Wright Muelas1, Emma Lundberg2, Douglas Kell1
1) University of Manchester, United Kingdom;
2) KTH Royal Institue of Technology, Stockholm, Sweden;

The expression levels of SLC or ABC membrane transporter transcripts typically differ 100- to 10,000-fold between different tissues. The Gini coefficient characterizes such inequalities and here is used to describe the distribution of the expression of each transporter among different human tissues and cell lines. Many transporters exhibit extremely high Gini coefficients even for common substrates, indicating considerable specialization consistent with divergent evolution. The expression profiles of SLC transporters in different cell lines behave similarly, although Gini coefficients for ABC transporters tend to be larger in cell lines than in tissues, implying selection. Transporter genes are significantly more heterogeneously expressed than the members of most non-transporter gene classes. Transcripts with the stablest expression have a low Gini index and often differ significantly from the “housekeeping” genes commonly used for normalization in transcriptomics/qPCR studies. PCBP1 has a low Gini coefficient, is reasonably expressed, and is an excellent novel reference gene. The approach, referred to as GeneGini, provides rapid and simple characterization of expression-profile distributions and general improved normalization of genome-wide expression-profiling data will be described.

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