DNA-metabarcoding sequencing calculator

Optimize your experimental design and estimate costs for soil biodiversity assessments

mdi-flask-outline Beta Version

mdi-flask-outline Experimental Design

Used when calculating the pooling effect—the impact of sample pooling on biodiversity estimates.

The number of distinct geographical or environmental locations being sampled

The number of individual samples collected at each site

The number of samples pooled into each pool or the total number of pools each site when using semi-pooled approach

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Costs

The cost per sample for DNA extraction, including reagents, consumables, and labor.

Cost of PCR amplification, indexing/adapter ligation, clean-up, and QC needed to create a sequencing-ready library from one sample.

Total reagent + flow-cell cost for a single sequencing run (e.g., one Illumina lane, PacBio SMRT Cell, or ONT flow cell), regardless of how many libraries are pooled.

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Sequencing Parameters

Required number of reads per individual sample

The target number of sequencing reads per individual sample needed for adequate coverage (applied to unpooled sampling).

Typically 5,000-50,000 for amplicon sequencing

Read loss during quality control (%)

Accounts for reads lost during bioinformatic processing steps including quality filtering, chimera removal, non-target amplicon filtering, and denoising.

Expected percentage of reads lost during bioinformatic processing (typically 30-60% for metabarcoding)

📊 Sequencing capacity analysis

Individual

Pooled

Semipools

Sequencing units per run:

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{{ sequencingCapacity.semiPooled.currentSamples.toLocaleString() }}

Max platform capacity:

Remaining capacity:

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{{ sequencingCapacity.pooled.remainingCapacity.toLocaleString() }}

{{ sequencingCapacity.semiPooled.remainingCapacity.toLocaleString() }}

Platform utilization:

{{ sequencingCapacity.unpooled.utilizationPercent }}%

{{ sequencingCapacity.pooled.utilizationPercent }}%

{{ sequencingCapacity.semiPooled.utilizationPercent }}%

Orange values: Exceed platform capacity and require multiple runs

Pooling factor

Pooling effect describes the difference between pooled samples and individual samples in their ability to estimate within-site diversity. As shown by Chen et al., 2025, pooling effect varies across taxa of interest and depends on the number of pooled individual samples (recommended not to exceed 9) and the number of reads allocated to the pooled sample compared to the total read counts of individual samples.

Pooling factor is ratio of the pooled sample's read count to the total reads across the individual samples

Number of reads assigned to each pooled sample at the selected pooling factor.

Please select taxonomic group.

Pooling effect is positive.

Pooling effect is negative.

Pooling effect is unknown (>100 samples per site).

Number of reads assigned to each semipool at the selected pooling factor.

Please select taxonomic group.

Pooling effect is positive.

Pooling effect is negative.

Pooling effect is unknown (>100 samples per site).

Ratio of sequencing reads given to one pooled sample versus the combined reads that would have been allocated to the individual samples it replaces. Example: 4 individual samples, with each getting 1000 reads, have 4000 reads in total for assessing biodiversity within a site. With a pooling factor of 0.5, the pooled sample has 2 000 reads, with a pooling factor of 5, it has 20 000 reads.

The sequencing technology platform that will be used (affects throughput and cost considerations)

The total number of sequencing reads produced in a single run by the selected platform (automatically set based on platform, but can be manually adjusted)

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Default for {{ sequencingPlatform }}: {{ platformThroughputs[sequencingPlatform].toLocaleString() }} reads

mdi-calculator Results

mdi-book-open-variant Citation

Chen M, Dulya O, Mikryukov V, Copot O, Metsoja M, Tedersoo L. (under review). Sampling design and sample processing affect soil biodiversity assessments.