Import QPCR raw data / open existing file

During this first step, you can: At any time, you can add or remove a plate from your project thanks to the corresponding icons. It is also possible to merge two different projects.

Plate settings

You can edit the data of each well separately or select and modify a group of wells. You also can change the targets and samples properties (name, efficiency of the primers), and remove or add new ones. You can disable wells in order to not take them into account for calculations.


Standard curve calculation

You can define as "standards" the wells that contains dilutions of DNA in order to calculate PCR efficiency. Then, you precise the amount of DNA (arbitrary unit) in the different wells and the program will plot the standard curve and calculate PCR efficiency for this set of primers. This efficiency will be taken into account for subsequent relative quantifications.


Reference target and sample

For relative quantification calculations, you must define one or several reference gene(s) and one reference sample. They can be either shared for all plates or specific of each plate.


Relative quantification

The wells defined as "unknown" are used to calculate relative quantifications. An improved ΔΔCt method (1) allows you to obtain reliable quantifications and error. The confidence level is modifiable and can be either gaussian or calculated using a T-test.


The program plots results as histograms that are easy to customize.


Results, export and save

Results can be printed or exported in a pdf file containing a table with all the data and plots for standard curves and/or relatives quantifications.


You can also save your project in the pyQPCR XML file format that allows you to keep the entire project with the different plates and settings easily recoverable.


A Help menu is available and summarize the different functionalities of the software.


(1) qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Hellemans J, Mortier G, De Paepe A, Speleman F, Vandesompele J. Genome Biol. 2007;8(2):R19.