📈 qPCR Efficiency Calculator
Convert the slope of a qPCR standard curve into amplification efficiency, and see the fold change per cycle.
Amplification efficiency
E = 10^(−1/slope) − 1
100.1%
Fold change per cycle
ideal is 2× (slope −3.32, 100%)
2.001×
How the qpcr efficiency calculator works
From a standard curve of Cq versus log(template), the amplification efficiency is E = 10^(−1/slope) − 1. A perfect reaction doubles the product each cycle, which corresponds to a slope of −3.322 and 100% efficiency. Acceptable assays generally fall in the 90-110% range (slope roughly −3.6 to −3.1).
A fixed formula from the standard curve, no reference data. Enter the slope your qPCR software reports (it is negative) to get the efficiency and per-cycle fold change.
Frequently asked questions
How do you calculate qPCR efficiency from the slope?
Efficiency E = 10^(−1/slope) − 1, expressed as a percentage. A slope of −3.32 gives 10^(1/3.32) − 1 = 2 − 1 = 1.00, i.e. 100% efficiency (the product doubles each cycle).
What slope means 100% efficiency?
−3.322. That is the slope at which each cycle exactly doubles the amount of product, which is the definition of 100% amplification efficiency.
What is an acceptable qPCR efficiency?
Generally 90-110%, corresponding to a standard-curve slope of about −3.6 to −3.1. Outside that range suggests inhibitors, pipetting error, poor primer design or a suboptimal standard curve.
What does the fold change per cycle mean?
It is 10^(−1/slope), how much the target amplifies each cycle. Ideal is 2× (doubling). A value below 2 means less than perfect efficiency; the reaction amplifies more slowly than doubling.
Why is the slope negative?
Because Cq (the cycle at which signal crosses threshold) decreases as template concentration increases, more starting template crosses threshold sooner. Plotting Cq against log(template) therefore gives a negative slope.
What is R² and why does it matter here?
R² measures how well the standard-curve points fit a straight line (aim for ≥ 0.98). This tool computes efficiency from the slope; a good R² is what makes that slope, and the efficiency, trustworthy.
How do I build the standard curve to get the slope?
Run a dilution series of a known template (commonly a 10-fold series over 5-6 points, in triplicate), record the Cq at each dilution, and plot Cq on the y-axis against log₁₀(template amount) on the x-axis. The slope of the best-fit line is what you enter here.
Why does efficiency matter for the ΔΔCq method?
The comparative ΔΔCq method assumes 100% efficiency (a perfect doubling each cycle) for both target and reference genes. If real efficiencies differ from that or from each other, relative-quantification results are skewed, so you either correct for the measured efficiency or use a curve-based absolute method instead.
What can push efficiency outside the 90-110% range?
Above ~110% usually points to PCR inhibitors carried over in the template, pipetting error in the dilution series, or primer-dimer/non-specific product; below ~90% suggests suboptimal primer design, degraded template, or annealing/extension conditions that need optimisation.
How does efficiency relate to the fold change over several cycles?
Each cycle multiplies the product by (1 + E), where E is the efficiency as a fraction. Over n cycles the amplification is (1 + E)ⁿ. At 100% efficiency (E = 1) that is 2ⁿ, a 10-fold dilution step spans about 3.32 cycles because 2^3.32 = 10, which is exactly why the ideal standard-curve slope is −3.32.
Why use a slope-based efficiency instead of assuming 100%?
Assuming a perfect doubling when the real reaction amplifies at, say, 95% per cycle compounds over 30+ cycles into a sizeable quantification error. Measuring the slope from a dilution series gives the actual efficiency, so you can either correct the relative-quantification maths or confirm the assay is close enough to 100% to use the simple ΔΔCq method.