Read Coverage Calculator

Calculator

Sample or project name

Reference or target size (bp)

Read count / read pairs

Average read length (bp)

Read layout

Mapped reads (%)

Duplicate reads (%)

On-target reads (%)

Desired coverage (×)

Example data table

Sample	Reference size (bp)	Read count / pairs	Read length	Layout	Mapped %	Duplicate %	On-target %	Effective coverage
Human WGS	3,000,000,000	600,000,000	150	Paired-end	96	12	100	57.60×
Exome Panel	45,000,000	60,000,000	150	Paired-end	94	18	72	370.46×
Amplicon Assay	150,000	1,200,000	250	Single-end	98	8	92	1,802.24×

Formula used

Total sequenced bases
Total sequenced bases = Read count × Average read length × Read multiplier

Mapped bases
Mapped bases = Total sequenced bases × (Mapped reads % ÷ 100)

Unique mapped bases
Unique mapped bases = Mapped bases × (1 − Duplicate reads % ÷ 100)

Usable bases
Usable bases = Unique mapped bases × (On-target % ÷ 100)

Coverage calculations
Gross coverage = Total sequenced bases ÷ Reference size
Mapped coverage = Mapped bases ÷ Reference size
Unique coverage = Unique mapped bases ÷ Reference size
Effective coverage = Usable bases ÷ Reference size

Estimated breadth
Estimated breadth ≈ [1 − e^{−effective coverage}] × 100

Reads required for a target coverage
Reads required = Desired coverage × Reference size ÷ [Average read length × Read multiplier × Mapped fraction × Unique fraction × On-target fraction]

How to use this calculator

Enter a sample name so the result report is clearly labeled.
Provide the reference or target size in base pairs. Use panel size for targeted assays and genome size for whole-genome work.
Enter the number of reads for single-end data or read pairs for paired-end data.
Type the average read length and choose the correct read layout.
Add mapping, duplicate, and on-target percentages from alignment or QC reports.
Set the desired coverage threshold, then calculate to view gross, mapped, unique, and effective depth.
Review the graph, estimated breadth, and reads still required to reach the target.
Use the CSV or PDF buttons after calculation to save the current result set.

FAQs

1. What is read coverage?

Read coverage describes how many times, on average, each base in a reference or target region is sequenced. Higher coverage usually improves detection confidence, especially for rare variants and uneven libraries.

2. What is the difference between gross and effective coverage?

Gross coverage uses all sequenced bases. Effective coverage removes losses from unmapped reads, duplicates, and off-target reads. Effective coverage better reflects the depth that actually supports downstream biological interpretation.

3. Why do duplicate reads reduce useful coverage?

Duplicate reads often represent repeated observation of the same original molecule. They inflate raw depth but add limited new information, so duplicate-adjusted coverage is usually more informative for analytical planning.

4. Why might on-target percentage be below 100%?

Target capture and amplicon workflows rarely place every read inside the intended region. Off-target alignment, nonspecific amplification, and library complexity all lower the usable fraction.

5. Does paired-end sequencing double the bases counted?

Yes. When the input value represents read pairs, paired-end layout contributes two reads per fragment. The calculator therefore multiplies read length by two before estimating total sequenced bases.

6. What coverage level is usually enough?

The right target depends on assay type, variant frequency, heterogeneity, and quality goals. Whole-genome projects may need modest depth, while somatic panels and amplicon assays often need much higher coverage.

7. Can this calculator be used for panels and amplicons?

Yes. Enter the total target region size instead of a full genome size. The on-target setting is especially important for capture panels, while duplicates can strongly affect effective depth in small assays.

8. Why is breadth estimated with a Poisson-style model?

The breadth estimate offers a quick theoretical approximation from average depth. Real experiments can deviate because of GC bias, uneven capture, repeat content, and local alignment difficulty.