Estimate mutation rate from observed mutant counts, background controls, screened cells, generations, and target length. The calculator reports per-locus rate, per-base rate, scaled rates, and a Poisson confidence interval for rare mutation events.
Load a preset assay or enter your own data. Results update as you change counts, generations, or target size.
Load a common genetics assay, then edit the counts, population size, generations, and target length.
Bacterial reversion assay
Estimate a locus-level mutation rate from counted revertant colonies.
Enter raw observed mutant counts and any background count from the control plate or control sample.
Corrected mutant count
38.0
observed mutants minus background mutants
Mutation opportunities equal screened cells or genomes multiplied by generations or cell divisions.
Live mutation rate estimate
This estimate uses 38.0 corrected mutants across 800,000,000 mutation opportunities. The count is large enough for a more stable teaching estimate.
corrected
38.0
opportunities
8.00 × 10^+8
bp rate
3.96 × 10^-11
Per locus per generation
4.750 × 10^-8
Per base per generation
3.958 × 10^-11
Per million cells
4.750 × 10^-2
Per billion copied bases
3.958 × 10^-2
Rare mutation counts follow counting noise. This interval approximates a 95% Poisson confidence range.
Per locus 95% interval
3.36 × 10^-8 to 6.52 × 10^-8
Per base 95% interval
2.80 × 10^-11 to 5.43 × 10^-11
Scaled outputs help compare a locus-level estimate with a base-pair-level estimate.
| Quantity | Formula | Value |
|---|---|---|
| Corrected mutants | observed − background | 38.0 |
| Mutation opportunities | population × generations | 800,000,000 |
| Locus mutation rate | corrected mutants ÷ opportunities | 4.750 × 10^-8 |
| Base-pair mutation rate | locus rate ÷ target bp | 3.958 × 10^-11 |
Mutation rate measures how often a new heritable DNA change appears during replication, cell division, or one organismal generation. A rate differs from a frequency because frequency counts mutants that already exist in the sampled population. Growth, selection, and bottlenecks can change frequency after the original mutation event.
Spontaneous mutations arise from replication errors, base damage, recombination errors, and imperfect DNA repair. OpenStax describes DNA polymerase mistakes, chemical mutagens, ultraviolet radiation, and repair mechanisms such as photoreactivation in its mutation chapter. Read the OpenStax mutation overview.
Researchers report mutation rates in several units. Microbial genetics often uses mutations per locus per generation. Sequencing studies often use mutations per base pair per generation. This calculator shows both, so colony screens and DNA sequence data can fit the same learning framework.
These buttons load realistic example values for reversion assays, yeast screens, sequencing panels, and small teaching data sets. They help students compare mutation-rate units quickly.
This card records observed mutants and background mutants. The calculator subtracts background so untreated control events do not inflate the final rate.
This card defines the denominator. Cells screened, genomes sampled, or individuals observed must be multiplied by generations or divisions before rate estimation.
Target size converts a locus-level rate into a per-base rate. Use the gene length, amplicon size, or sequencing panel length in base pairs.
Mutation events often appear as rare counts. The Poisson interval shows how much small counts can move the estimated rate up or down.
This button saves a compact result card for lab reports, classroom slides, or comparison between control and treatment conditions.
Type the number of mutant colonies, sequence variants, or resistant cells detected in the assay.
Add the control or untreated mutant count so the calculator can estimate the corrected mutation signal.
Enter screened cells, genomes, or individuals, then enter generations or cell divisions.
Use the gene, locus, amplicon, or sequencing panel length to convert the locus rate into a base-pair rate.
Compare the per-locus rate, per-base rate, scaled rates, and Poisson confidence interval.
A plate shows 42 revertants, while the control shows 4 background revertants. The corrected count equals 38. If the experiment screened 100,000,000 cells through 8 generations, the opportunity count equals 800,000,000 cell-generations.
The locus rate equals 38 ÷ 800,000,000, or 4.75 × 10−8 per locus per generation. If the target contains 1,200 bp, the per-base estimate equals 3.96 × 10−11 per base per generation.
A sequencing panel detects 7 de novo variants across 5,000 individuals. Each individual contributes one generation, and the analysed target length equals 2,000,000 bp. The opportunity count equals 5,000 genome-generations for the locus panel.
The panel-level rate equals 7 ÷ 5,000, or 1.4 × 10−3 per panel per generation. Dividing by 2,000,000 bp gives 7.0 × 10−10 per base per generation.
Mutation introduces new alleles into populations. Population genetics then asks whether drift, selection, migration, or nonrandom mating changes those alleles after they arise. OpenStax explains mutation as one mechanism that can change allele frequencies, though many individual mutations remain neutral or rare.
Microbiology labs use mutation-rate estimates to compare untreated controls with chemical or radiation exposure. Cancer biology uses a related idea when it studies DNA repair deficiency and mutational load. Evolutionary genetics uses rate estimates to model molecular clocks, adaptation, and long-term genome stability.
Drake, Charlesworth, Charlesworth, and Crow reviewed spontaneous mutation rates across microbes, viruses, and eukaryotes in Genetics. Their 1998 review remains a widely cited source for comparing mutation-rate scales across organisms. Read the spontaneous mutation rate review.
This calculator uses a direct opportunity model. That model works well for classroom problems, selected colony screens, and first-pass sequencing estimates. It does not replace fluctuation-test methods for jackpot-prone microbial cultures.
Mutation detection also depends on assay sensitivity. A phenotype screen may miss synonymous variants, mild missense variants, lethal mutations, and mutations outside the scored target. A sequencing panel can miss low-level mosaic variants or regions with poor coverage.
Use this tool for genetics education, assay planning, and transparent rate calculations. Do not use it as a clinical diagnostic report or as the only evidence for mutagen safety decisions.
Use these tools to connect mutation with allele-frequency change and population-level inheritance models.