Microbiome Diversity Calculator

Calculate OTU-based alpha diversity metrics for microbiome analysis.

OTU richness (number of OTUs observed) (OTUs)

Total reads in sample (reads)

Singletons f1 (OTUs with 1 read) (OTUs)

Shannon diversity H' estimate (nats)

Chao1 estimated richness (OTUs)

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Why Compositional Data Needs Care

Sequencing returns relative abundances that sum to one. This has a consequence that trips up most first-time analyses: if one taxon increases, every other must appear to decrease, whether or not its absolute abundance changed at all.

Standard statistical tests assume independent variables. Compositional data violates that assumption, which is why the field uses centred log-ratio transformation or dedicated methods such as ANCOM and ALDEx2 rather than applying ordinary tests to proportions.

Metric	What it measures	Note
Shannon H′	Alpha diversity, sensitive to rare taxa	Requires rarefaction to compare
Chao1	Estimated richness	Lower bound; needs singleton data
Bray–Curtis	Beta diversity with abundance	Common default
UniFrac	Beta diversity using phylogeny	Weighted or unweighted variants

OTU or ASV?

	OTU	ASV
Definition	Sequences clustered at 97% identity	Exact sequence variants
Resolution	Approximate species level	Single nucleotide
Comparability	Study-specific clusters	Comparable between studies
Status	Legacy	Current standard

ASVs have largely replaced OTUs because they are defined by the sequence itself rather than by clustering within a particular dataset. That makes them directly comparable across studies and reproducible, whereas OTU boundaries shift depending on what else is in the sample.

Interpreting Diversity Changes

Reduced alpha diversity is a common finding in disease states and is often described as dysbiosis. Two cautions apply: the association is correlational and frequently a consequence of illness rather than a cause, and higher diversity is not universally better — the healthy vaginal microbiome is characteristically low-diversity and Lactobacillus-dominated.

Worked Examples

Example 1: Healthy gut: 150 OTUs, 10000 reads, f1=40

Coverage=1-40/10000=99.6%

Result: Chao1=150+1600/220=157 — good coverage

Low singletons → good sampling completeness

Example 2: Diseased gut: 80 OTUs

Reduced alpha diversity

Result: Dysbiosis often associated with lower diversity

IBD, obesity, C. diff: lower gut diversity

Example 3: The compositional trap

One taxon expands from 10% to 50%

Result: All others appear to fall

Their absolute abundances may be unchanged. Only the proportions shifted, which is why compositional methods are necessary.

Example 4: When low diversity is normal

Healthy vaginal microbiome, Lactobacillus-dominated

Result: Low diversity is the healthy state

Applying a gut-derived assumption that more diversity is better would misclassify a normal community as dysbiotic.

Common Mistakes

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Applying standard statistics to relative abundances

Compositional data sums to a constant, so components are not independent. Use CLR transformation or compositional methods such as ANCOM.

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Comparing diversity across unequal sequencing depths

Deeper sequencing detects more taxa. Rarefy to a common depth or use coverage-based standardisation.

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Assuming higher diversity is always healthier

The healthy vaginal microbiome is low-diversity by design. Context determines whether diversity is desirable.

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Treating dysbiosis as causal

Reduced diversity in disease is usually correlational and often a consequence of illness, medication or diet change rather than a driver.

Frequently Asked Questions

OTU vs ASV?

OTU (Operational Taxonomic Unit): clustered at 97% 16S similarity. ASV (Amplicon Sequence Variant): exact sequences, higher resolution. Modern pipelines (DADA2, Deblur): use ASVs. ASVs: can be compared across studies without re-clustering.

Rarefaction for comparison?

Different samples: different read depths. Rarefy to minimum sample size to compare α diversity fairly. Debate: rarefying throws away data. Alternative: model-based normalization (DESeq2, TMM). For richness: rarefaction still widely used.

What is the difference between OTUs and ASVs?

OTUs cluster sequences at 97% identity within a dataset; ASVs are exact sequence variants resolved to single nucleotides and comparable across studies.

Why is microbiome data called compositional?

Because sequencing gives relative abundances summing to one. An increase in one taxon forces apparent decreases in others regardless of absolute change.

Why rarefy before comparing samples?

Deeper sequencing detects more taxa, so diversity estimates scale with read depth. Rarefaction or coverage standardisation makes comparison valid.

Is higher microbiome diversity always better?

No. Gut communities generally benefit from diversity, but the healthy vaginal microbiome is characteristically low-diversity and Lactobacillus-dominated.