qPCR Quantitation

Generate standard curves and quantify unknown samples from qPCR data

1Input values for standards.

Unit:

Reps:

Conc (ng)	Average	Ct 1	Ct 2	Ct 3

Add at least 5 valid data points to generate results.

2Input for each sample to be quantified.

Sample Name	Dilution (1:X)	Cq 1	Cq 2	Cq 3	Avg Cq	Undiluted Amt
					-	-

Calculation Formulas & User Guide

Learn how to use qPCR quantitation with standard curve analysis

Overview

This tool performs absolute quantification of unknown samples using a standard curve generated from known concentrations. The standard curve is created by plotting Cq (quantification cycle) values against the logarithm of template concentrations, allowing precise calculation of unknown sample amounts.

How to Use

Prepare Standard Curve Data:
- Select your unit (µg, ng, pg, or copies)
- Set the number of technical replicates (1-5)
- Enter known concentrations for each standard point
- Input Ct values for each replicate
- Alternatively, input the average Ct value directly
- Use “Example” button to load demo data
Review Standard Curve Results:
- Check R² value (should be ≥ 0.98 for reliable results)
- Verify PCR efficiency (90-110% is ideal)
- Examine the standard curve plot for outliers
Quantify Unknown Samples:
- Add samples using “Add Sample” button
- Enter sample names and dilution factors (if applicable)
- Input Cq values for each replicate
- View calculated amounts in the “Undiluted Amt” column
- Export results using “Export CSV” button

Calculation Formulas

1. Standard Curve (Linear Regression)

Cq = m × log₁₀(Concentration) + b

Cq = Quantification cycle (Ct value)

m = Slope of the regression line

b = Y-intercept

Concentration = Template amount in standards

2. PCR Efficiency

Efficiency (%) = (10^(-1/slope) - 1) × 100

Ideal efficiency: 90-110% (slope ≈ -3.32 for 100% efficiency)

3. Sample Quantification

log₁₀(Concentration) = (Cq - b) / m

Rearranging to solve for concentration:

Concentration = 10^((Cq - b) / m)

Undiluted Amount = Concentration × Dilution Factor

If sample was diluted 1:10 before qPCR, multiply calculated concentration by 10 to get original amount

4. Coefficient of Determination (R²)

R² measures how well the regression line fits the standard curve data points.

R² ≥ 0.98: Excellent fit, reliable quantification
R² = 0.95-0.98: Acceptable for some applications
R² < 0.95: Poor fit, results may be unreliable

Important Notes

Standard Curve Quality: Always check R² and efficiency values. Poor curve quality will lead to inaccurate quantification.
Concentration Range: Standard curve should span the expected range of unknown samples. Extrapolation beyond the curve range is not recommended.
Outliers: If a replicate Ct value differs significantly from others, consider removing it from the analysis.
Dilution Factor: If your samples were diluted before qPCR analysis, enter the dilution factor (e.g., for 1:10 dilution, enter 10).
Technical Replicates: At least duplicate or triplicate measurements are recommended for both standards and samples to ensure reproducibility.
Units: All calculations assume your standards and samples use the same unit. The tool automatically scales output to appropriate units (e.g., ng → µg → mg).
No Template Control (NTC): Always include NTC in your qPCR run. Ct values from NTC should be much higher than samples or absent.

Best Practices

Use at least 5 standard points covering 4-5 orders of magnitude
Prepare standards fresh or use properly stored aliquots to avoid degradation
Ensure consistent pipetting technique across all standards and samples
Include at least one negative control (no template control, NTC)
Run standards and samples in the same qPCR plate when possible
Store raw Ct values and standard curve parameters for future reference
Verify standard curve linearity before proceeding with sample quantification