illumina coverage calculator

What is sequencing coverage?

Coverage (often written as depth, such as 30x or 100x) is the average number of times each base in a reference genome is observed by sequencing reads. In Illumina experiments, higher coverage usually improves confidence in variant calls, especially for low-frequency variants and challenging genomic regions.

A simple way to think about it: if you sequence 300 billion bases against a 3 billion base genome, your raw average depth is about 100x. In real data, not all bases are equally represented, and some reads are filtered out, so practical depth is lower than the theoretical maximum.

Coverage formula used by this calculator

Raw (theoretical) coverage

Raw coverage = (Reads × Read length × Read pairs factor) ÷ Genome size

• Read pairs factor is 2 for paired-end and 1 for single-end.
• Genome size is converted from Mb to bp.

Effective coverage

Effective coverage = Raw coverage × (Alignment rate) × (1 − Duplicate rate)

• Alignment rate accounts for reads that map successfully.
• Duplicate rate accounts for non-unique reads that often do not add independent evidence.

How to use this illumina coverage calculator

• Enter your organism’s genome size in megabases (Mb).
• Input expected total reads in millions.
• Set read length and single-end vs paired-end mode.
• Adjust alignment and duplicate assumptions to match your pipeline history.
• Optionally set a target depth to estimate required read count.

Worked examples

Example 1: Human whole genome (WGS)

Suppose you plan 600 million paired-end reads at 150 bp for a 3,200 Mb genome. Raw coverage is high, but after accounting for mapping losses and duplicates, effective depth may land closer to the practical range expected for production WGS.

Example 2: Bacterial isolate

For a 5 Mb bacterial genome, even modest read counts can produce very high depth. In that case, you may optimize for sample multiplexing and cost rather than maximizing depth further.

Choosing a target depth by application

• Germline WGS: commonly around 30x effective depth.
• Tumor-normal studies: often higher, with tumor depth increasing as purity decreases.
• Targeted panels: typically much higher depth to detect low VAF variants.
• RNA-seq: “coverage” is interpreted differently; read count planning is usually transcript-focused.

Why planned and observed coverage differ

1) Library complexity

Low complexity increases duplicate rate and reduces unique evidence. Improving input quality and library prep often yields bigger gains than simply adding more reads.

2) Read quality and trimming

Adapter and low-quality trimming remove bases before alignment. This lowers effective sequenced bases, particularly in low-quality runs.

3) Mapping challenges

Repetitive regions, contamination, and reference mismatch can lower alignment rates. If your organism is diverged from the reference, include a more conservative alignment assumption.

4) Uneven coverage distribution

Average depth does not guarantee all loci are covered equally. GC bias, capture bias, and local sequence properties can leave clinically relevant sites under-covered despite acceptable mean depth.

Practical planning tips

• Use historical QC metrics from your own lab for alignment and duplicate defaults.
• Plan with a buffer (for example 10–20%) when minimum depth thresholds are strict.
• Track both mean depth and breadth of coverage in post-run QC.
• For sensitive applications, validate the plan with pilot data.

Final note

This calculator is designed for fast planning and sanity checks, not as a replacement for full experimental design or regulatory-grade assay validation. Still, it gives a clear first-pass estimate of how many Illumina reads you need to hit your depth goals.