Q score sequencing calculator form
Choose a calculation mode, enter the known values, and submit. Only the fields needed for the selected mode will drive the result.
Example data table
These sample rows show how typical sequencing quality values translate into error probability, accuracy, and read-level expectations.
| Sample | Q Score | Error Probability | Accuracy | Read Length | Expected Errors/Read | Pass Q30 |
|---|---|---|---|---|---|---|
| Library A | 20 | 1.000000E-02 | 99.000000% | 150 | 1.500000 | No |
| Library B | 25 | 3.162278E-03 | 99.683772% | 150 | 0.474342 | No |
| Library C | 30 | 1.000000E-03 | 99.900000% | 150 | 0.150000 | Yes |
| Library D | 35 | 3.162278E-04 | 99.968377% | 250 | 0.079057 | Yes |
| Library E | 40 | 1.000000E-04 | 99.990000% | 300 | 0.030000 | Yes |
Formula used
Core sequencing formulas
Q score: Q = -10 × log10(Perror)
Error probability: Perror = 10^(-Q/10)
Accuracy percentage: Accuracy = (1 - Perror) × 100
Expected error formulas
Expected errors per read: Read Length × Perror
Total expected errors: Read Length × Total Reads × Perror
Batch pass rate: (Values ≥ Threshold ÷ Total Values) × 100
The Q score is logarithmic, so each increase of 10 reduces the estimated error probability by a factor of ten.
How to use this calculator
Single value workflow
- Select the matching conversion mode.
- Enter sample name, threshold, and read details.
- Fill the one main input for that mode.
- Click calculate to show the result above the form.
- Review the graph, threshold status, and expected errors.
Batch summary workflow
- Choose batch summary mode.
- Paste Q scores separated by commas or new lines.
- Set the threshold value, such as Q30.
- Submit to view average, median, pass rate, and chart.
- Use the export buttons to save the report.
FAQs
1. What is a Q score in sequencing?
A Q score is a logarithmic measure of base-calling confidence. Higher values mean lower error probability. Q20 suggests about one error in 100 bases, while Q30 suggests about one error in 1,000 bases.
2. Why is Q30 often used as a benchmark?
Q30 is widely used because it represents 99.9% estimated accuracy. It is strict enough for many teaching, lab, and review tasks while still being realistic for common sequencing workflows.
3. Does a higher Q score always guarantee perfect data?
No. A high Q score indicates strong base-level confidence, but overall data quality also depends on alignment quality, coverage depth, contamination, library preparation, and downstream analysis choices.
4. Why does the calculator use logarithms?
Sequencing error probabilities can become very small. A logarithmic scale keeps the numbers easier to compare and makes each 10-point increase meaningful as a tenfold reduction in estimated error probability.
5. What does expected errors per read mean?
It estimates how many incorrect bases you might expect, on average, across one read of the chosen length. It is not a promise for every read, but a planning and interpretation aid.
6. When should I use batch summary mode?
Use batch summary mode when you have multiple Q values from positions, reads, or summary outputs. It helps you see average quality, spread, threshold pass rate, and overall reliability more quickly.
7. Can I convert accuracy directly into Q score?
Yes. The calculator converts accuracy into error probability first, then applies the Q score formula. This is useful when a report gives accuracy percentages instead of raw Q values.
8. Why do very high accuracy values produce huge Q scores?
As accuracy approaches 100%, error probability approaches zero. Because the Q formula uses a negative logarithm, extremely tiny error probabilities produce very large Q scores and, at exactly 100%, a theoretical infinity.