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.
|Back to GQ2019 overview page|