No black-box scores.
Exactly how we read your microbiome — and why we won’t fake a number. The category’s biggest weakness is the opaque, horoscope-style score that no one can explain. Ours is the opposite: every step below is inspectable, and every claim is bounded by what the method can actually support.
From swab to a real reading
Four steps take your sample from raw DNA to a composition you can actually interrogate. None of it is proprietary magic — it is the current standard for microbiome composition, done carefully and named honestly.
16S rRNA sequencing (V3–V4)
The lab sequences the V3–V4 hypervariable regions of the bacterial 16S rRNA gene — the standard marker for identifying which bacteria are present in a stool sample. It reads the community as a whole rather than culturing a handful of species, so rare and hard-to-grow gut bacteria still get counted.
DADA2 denoising and ASV inference
Raw reads are run through DADA2, which models and removes sequencing error to resolve exact amplicon sequence variants (ASVs) instead of the older, coarser OTU clustering. ASVs are reproducible, single-nucleotide-resolution units, so the same biology produces the same identifiers run to run.
Taxonomy assigned against GTDB
Each ASV is classified against the Genome Taxonomy Database (GTDB), a modern, genome-based reference. This is where a lot of consumer tests quietly go wrong — see below on why the reference database is not a footnote.
Genus-level relative abundance
We report composition as genus-level relative abundance — the share of your community each genus represents — alongside diversity measures. This is the resolution 16S can support honestly, and it is what we show you.
A modern reference, not an outdated one
Naming a bacterium is only as good as the reference you name it against. Many 16S tests still lean on Greengenes, a reference that has not kept pace with how gut genera are classified today. The result is quiet mislabeling: modern gut taxa get filed under stale or wrong names, and that distortion is invisible in the final report.
We assign taxonomy against GTDB, a genome-based database that reflects the current understanding of bacterial relationships. Taxa come back named correctly. It is an unglamorous choice that most people will never notice — which is exactly why it is the kind of thing an honest test should get right.
Same sequencing, different reference database, different answer. When a test won’t tell you which reference it used, that is worth noticing.
The same sample, the same result
Lab-grade rigor most consumer tests skip: a result you can trust is one you can reproduce and trace back to the exact method that produced it.
Reproducible by design
The same sample, run through the same pipeline, yields the same result. DADA2’s exact-sequence approach and a fixed reference database remove the run-to-run drift that makes some tests feel like they change their mind.
Frozen to a method version
Every result is pinned to the exact pipeline version and reference-database version that produced it. We record what read your sample, not just what it found.
Comparable within a locked version
Because references evolve, results are only directly comparable within the same locked method version. We tell you which version you are on rather than silently comparing across incompatible methods.
We won’t show you a score we can’t defend.
Today we report your microbiome composition — who is present and in what proportion. The single numeric Biome Score is deliberately not on your report yet.
Before we display a number, it has to be validated against a real, method-matched reference cohort — samples run through the exact same 16S, DADA2, and GTDB pipeline, so the comparison is apples to apples. Until that work stands up, a score would be a confident-looking guess, and we would rather show you nothing than something we can’t defend.
This is a deliberate contrast. Plenty of tests ship a bold number on day one with thin validation behind it. That number feels like an answer and behaves like a horoscope.
We are choosing the slower, more honest path: composition now, the score when the evidence supports it. If that costs us a flashier report in the meantime, that is a trade we are comfortable making — because the whole point of Biome Atlas is a number you could actually stand behind.
What 16S can and can’t tell you
A method is only trustworthy if you are equally clear about where it stops. 16S is excellent for composition and diversity — and it is not a strain-level or functional assay. We don’t pretend otherwise.
What it can do well
- Identify which bacteria are present, down to the genus level.
- Measure the relative abundance of each genus in your community.
- Describe overall diversity and community structure.
- Reproduce the same reading from the same sample.
What we don’t claim
- Resolve individual strains — 16S does not reliably separate species or strains within a genus.
- Directly measure microbial activity or function; where we infer function, we say we are inferring it.
- Diagnose, treat, cure, or prevent any disease. This is a wellness and educational tool, not a medical device.
- Tell you a bacterium is “good” or “bad” in isolation — context and composition matter more than any single line.
A weak sample gets re-run, not guessed
Not every sample sequences cleanly. Low read depth, degraded DNA, or ambiguous signal all reduce how much a result can be trusted. When a sample falls below our quality bar, it is flagged and re-tested rather than scored blindly. We would rather ask for another sample than hand you a confident reading built on thin data.
How we handle your data
Transparency about the science is only half the story. Health-adjacent data belongs in a separate, security-first system — read how we’re designing activation, lab ingest, result delivery, and consent.