Considering Amplification Efficiencies

Qbase+ calculates an amplification efficiency (E) for each primer pair (= gene). Genes have different amplification efficiencies because:

  • some primer pairs anneal better than others
  • the presence of inhibitors in the reaction mix (salts, detergents…) decreases the amplification efficiency
  • inaccurate pipetting

Qbase+ has a parameter that allows you to specify how you want to handle amplification efficiencies on the Amplification efficiencies page.

How to specify the amplification efficiencies strategy you want to use?

Since we have included a dilution series for creating a standard curve in our qPCR experiment, we will select

  • Use assay specific amplification efficiencies
  • Calculate efficiencies from included standard curves

Amplification efficiencies are calculated based on the Cq values of a serial dilution of representative template, preferably a mixture of cDNAs from all your samples. Since you know the quantity of the template in each dilution, you can plot Cq values against template quantities for each primer pair. Linear regression will fit a standard curve to the data of each gene, and the slope of this curve is used to calculate the amplification efficiency.

How to check the amplification efficiencies of the genes?

Once you have made this selection, qbase+ starts calculating the efficiencies and the results are immediately shown in the calculation efficiencies table.

In this way, one amplification efficiency (E) for each gene is calculated and used to calculate Relative Quantities (RQ): ∆Cq is calculated for each well by subtracting the Cq of that well from the average Cq across all samples for the gene that is measured in the well. So ∆Cq is the difference between the Cq value of a gene in a given sample and the average Cq value of that gene across all samples. Cq is subtracted from the average because in this way high expression will result in a positive ∆Cq and low expression in a negative ∆Cq. So at this point the data set contains one RQ value for each gene in each sample.

Click Next to go to the Normalization page.