Copy Number Variation (CNV) Calculator
Estimate relative copy number using the qPCR 2-ΔΔCt method. Enter your sample and calibrator Ct values below.
What is a CNV calculator?
A CNV calculator helps you estimate copy number variation from qPCR data. Copy number variation refers to gains or losses of DNA segments compared with a reference genome. In practical lab workflows, researchers often use Ct values from a target gene and a stable reference gene, then normalize against a calibrator sample.
This page provides a quick, practical tool for that workflow. If you are screening for deletions, duplications, or relative genomic dosage changes, this calculator gives you an immediate estimate to support downstream interpretation.
How this CNV calculation works
The calculator uses the classic comparative Ct method:
ΔCt(calibrator) = Ct(target, calibrator) − Ct(reference, calibrator)
ΔΔCt = ΔCt(sample) − ΔCt(calibrator)
Relative Quantity (RQ) = 2−ΔΔCt
Estimated Copy Number = RQ × (known calibrator copy number)
If your calibrator is known diploid for the locus, set calibrator copy number to 2 (default). The output then estimates the sample’s copy number relative to that baseline.
Input fields explained
1) Target Gene Ct (Sample)
The Ct value for the gene/region where you suspect copy number changes.
2) Reference Gene Ct (Sample)
A stable control gene used for normalization in the same sample.
3) Target and Reference Ct (Calibrator)
Values from a control sample with known copy number status. This provides the baseline for relative comparison.
4) Known Calibrator Copy Number
Usually 2 for autosomal diploid regions, but this can vary for sex chromosomes or custom controls.
Interpreting CNV output
- ~2 copies (with diploid calibrator): often consistent with expected dosage.
- Lower than expected: may suggest deletion or copy loss.
- Higher than expected: may suggest duplication or gain.
Biological interpretation should always consider assay quality, replicate consistency, and orthogonal confirmation methods (such as digital PCR, MLPA, or sequencing-based CNV calling).
Best practices for reliable CNV analysis
- Use technical replicates and average Ct values before final interpretation.
- Validate that amplification efficiencies for target and reference assays are similar.
- Choose a reference gene with proven stability in your sample type.
- Include positive and negative controls in every run.
- Confirm borderline calls with an independent method.
Common pitfalls
Using unstable reference genes
An unstable reference can skew ΔCt and produce false CNV calls.
Ignoring assay efficiency mismatch
The 2-ΔΔCt model assumes similar efficiencies. Large differences can bias fold-change and copy number estimates.
Over-interpreting single measurements
CNV decisions should not rely on one replicate or one run. Reproducibility is critical.
Final note
This CNV calculator is ideal for rapid estimation and educational use. For clinical or publication-grade reporting, pair these results with validated laboratory pipelines, quality controls, and professional review.