How to use (3 steps)
- Select an example or enter target size (genome size / total target region).
- Enter read count (PE/SE) or total yield (Gb).
- Average depth appears. Adjust factors or use inverse calculation if needed.
This is an estimate. Real coverage is not uniform and varies with duplicates, GC bias, design, and analysis. Verify with QC coverage distributions.
Inputs
—
Target size
WGS uses genome size; Exome/Panel uses total target size (bp).
Examples: 3.2 Gb (human WGS approx) / 50 Mb (human exome approx)
Input mode
Factors (0–1)
You can keep defaults for a rough estimate.
—
Options
≥k× is a Poisson-based guide assuming uniform random coverage. Real data varies (at high depth, a normal approximation may be used).
Results (average depth)
Intermediate calculations (raw → usable → mapped → on-target → unique)
| step | bases (auto units) |
|---|
≥k× proportion (guide)
| threshold | P(depth ≥ k) |
|---|
Guide based on Poisson with mean depth λ (not definitive due to non-uniform coverage).
How it’s calculated
- Compute raw_bases from read counts (PE/SE) or total yield (Gb).
- Apply usable, mapping, on-target, and duplicate rates (1-duplicate) to estimate unique on-target bases.
- Average depth = unique_on_target_bases / target_size.
- ≥k× can be shown as a Poisson-based guide.
Average depth is useful, but uniformity is separate. Confirm with coverage distribution QC.
How to use this calculator effectively
This guide helps you use NGS coverage calculator (average depth) in a repeatable way: define a baseline, change one variable at a time, and interpret outputs with explicit assumptions before you share or act on results.
How it works
The page applies deterministic logic to your inputs and shows rounded output for readability. Treat it as a comparison workflow: run one baseline case, adjust a single parameter, and measure both absolute and percentage deltas. If a result seems off, verify units, time basis, and sign conventions before drawing conclusions. This approach keeps your analysis reproducible across teammates and sessions.
When to use
Use this page when you need a fast estimate, a classroom check, or a practical what-if comparison. It works best for planning and prioritization steps where you need direction and magnitude quickly before investing in deeper modeling, manual spreadsheets, or formal external review.
Common mistakes to avoid
- Changing multiple parameters at once, which hides the true cause of output movement.
- Mixing units (percent vs decimal, monthly vs yearly, gross vs net) across scenarios.
- Comparing with another tool without aligning defaults, constants, and rounding rules.
- Using rounded display values as exact downstream inputs without re-checking precision.
Interpretation and worked example
Run a baseline scenario and keep that result visible. Next, modify one assumption to reflect your realistic alternative and compare direction plus size of change. If the direction matches your domain expectation and the size is plausible, your setup is usually coherent. If not, check hidden defaults, boundary conditions, and interpretation notes before deciding which scenario to adopt.
See also
FAQ
How many reads are needed for 30× WGS?
It depends on target size, read length, and factors (mapping rate, etc.). Use inverse calculation for your conditions.
What is the on-target rate?
A rough fraction of usable reads that land on the target region (varies by kit/conditions).
If the average depth is the same, is every region the same depth?
No. The average is just an average, and real coverage varies. Use this as a guide.
Is the ≥k× proportion accurate?
It is a Poisson-based guide assuming uniform random coverage. Real data varies.
What should I do first on this page?
Start with the minimum required inputs or the first action shown near the primary button. Keep optional settings at defaults for a baseline run, then change one setting at a time so you can explain what caused each output change.
How to use NGS coverage calculator (average depth) effectively
What this calculator does
This page is for estimating outcomes by changing inputs in one controlled workflow. The model keeps your focus on variables, not output shape. Start with stable assumptions, then test sensitivity by changing one key input at a time to observe directional impact.
Input meaning and unit policy
Each input has an expected unit and a typical range. For reliable interpretation, check whether you are using the same unit system, period, and base assumptions across all runs. Unit mismatch is the most common source of unexpected drift in numeric results.
Use-case sequence
A practical sequence is: first run with defaults, then create a baseline log, then run one alternative scenario, and finally compare only the changed output metric. This sequence reduces cognitive load and prevents false pattern recognition in early experiments.
Common mistakes to avoid
Avoid changing too many variables at once, mixing incompatible data sources, and interpreting a one-time output without checking robustness. A single contradictory input can flip conclusions, so keep each experiment minimal and document assumptions as part of your note.
Interpretation guidance
Review both magnitude and direction. Direction tells you whether a strategy moves outcomes in the desired direction, while magnitude helps you judge practicality. If both agree, you can proceed; if not, rebuild the baseline and verify constraints before deciding.
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