Stefan Rödiger 1,*,Andrej-Nikolai Spiess 2 , Michał Burdukiewicz 3
1Brandenburg University of Technology Cottbus–Senftenberg, Senftenberg, Germany
2 University Medical Center Hamburg-Eppendorf, Hamburg, Germany
3 University of Wroclaw, Wroclaw, Poland;
Quantitative PCR (qPCR) and digital PCR (dPCR) are widely employed DNA quantification methods in diagnostic and foren- sic applications. Both have proven to be precise and robust. In qPCR, both parameters quantification cycle (Cq) and amplification efficiency (AE) are estimated from the amplification curve data to quantify the DNA amount [1,2]. dPCR, which astonishingly predates qPCR, allows copy number quantification based on parti- tioning a PCR bulk reaction into multiple smaller reaction entities. Quantification is then achieved by assessing the distribution of tar- get molecules within the sample by Poisson statistics . Unlike qPCR, dPCR does not require a calibration curve for quantifica- tion. Moreover, according to recent reports, dPCR offers a more precise and reproducible quantification as well as sensitive detec- tion of minority genetic targets. Numerous platform technologies (e. g., droplet-based, chamber-based, microbead-based) have been developed so far, however their accompanying dPCR software mod- ules are exclusively tied to the platform and therefore lack general applicability . As the analysis of the dPCR and qPCR data is still matter of ongoing research, others and we have recently investi- gated different software frameworks to fill this gap [5,6]. This talk will discuss novel and specific advances within the field of dPCR and qPCR data analysis, which have been made available to the sci- entific community. The talk will focus on open source software and selected algorithms that can be used to build customized desktop and remote browser applications.
 S. Pabinger, et al., A survey of tools for the analysis of quantitative PCR (qPCR) data, Biomol. Detect. Quantif. 1 (2014) 23–33, http://dx.doi.org/10.1016/j.bdq. 2014.08.002.
 A.-N. Spiess, et al., Impact of smoothing on parameter estimation in quantitative DNA amplification experiments, Clin. Chem. 61 (2015) 379–388, http://dx.doi.org/10.1373/clinchem.2014.230656.
 M. Burdukiewicz, et al., Methods for comparing multiple digital PCR experiments, Biomol. Detect. Quantif. 9 (2016) 14–19, http://dx.doi.org/10. 1016/j.bdq.2016.06.004.
 S. Rödiger, et al., R as an Environment for the Reproducible Analysis of DNA Amplification Experiments, R J. 7 (2015) 127–150, http://journal.r-project.org/archive/2015-1/RJ-2015-1.pdf.
 A.-N. Spiess, et al., System-specific periodicity in quantitative real-time polymerase chain reaction data questions threshold-based quantitation, Sci. Rep. 6 (2016) 38951, http://dx.doi.org/10.1038/srep38951.
 S. Rödiger, et al., chipPCR: an R package to pre-process raw data of amplification curves, Bioinformatics. 31 (2015) 2900–2902, http://dx.doi.org/ 10.1093/bioinformatics/btv205.
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