Commissioning a 16s sequencing service: choosing the variable region against what you need to resolve, why the negative control is the most important sample you will send, and where amplicon data stops being able to answer the question

Specifying the work

1. Choose the variable region for what you need to distinguish. Different hypervariable regions resolve different taxa, and no single region resolves everything. A commonly used pair of adjacent regions gives broad coverage; a single region gives better sequence quality per read. Ask the provider which primers they use and what that choice cannot see.
2. Send negative controls, and insist they are sequenced. Extraction blanks and no-template controls carried through the whole process are how kit and reagent contamination is identified. On low biomass samples, contamination is frequently most of the signal, and without blanks it is indistinguishable from biology.
3. Send a mock community as a positive control. A defined mixture of known organisms at known proportions shows what the whole pipeline does to composition. It is the only way to know whether a shift you observe is biology or a bias in extraction, amplification or the classifier.
4. Fix the extraction, because it biases the answer. Gram-positive organisms with tough walls need mechanical lysis, and a gentle extraction under-represents them systematically. Use one extraction method for the whole study and describe it, because comparing across extraction methods compares the methods.
5. Agree the analysis, including the reference database version. Taxonomic assignment depends on the classifier and the reference database version, and results change when either does. Name them in the methods, and reanalyse a study's samples together rather than in batches across a database update.

Sample collection decides more than the sequencing

Time to preservation, the preservative used and the storage temperature all shift community composition, and they shift it differently for different organisms. Fix a collection protocol and apply it to every sample in the study.

Record collection metadata properly at the time. Reconstructing which samples were frozen late, a year later, is the part every microbiome study underestimates.

Batch effects, which are large here

Extraction batch, amplification batch and sequencing run all leave a signature large enough to look like a treatment effect. Randomise groups across every batch and record the batch so it can be modelled.

Where a study spans months, include repeated control samples in every batch. Without them a batch effect cannot be separated from a time effect.

Reading the result without overreaching

Differences in proportion are differences in proportion. A taxon rising because another fell is not the same claim as a taxon increasing in absolute terms, and the two are routinely conflated in discussion sections.

Report the primers, the region, the extraction, the classifier and the database version. Without those five, another laboratory cannot compare its numbers with yours, and most published comparisons that disagree differ on one of them.

Common questions

Can a 16s sequencing service identify organisms to species?

Rarely and unreliably. Short amplicon regions generally resolve to genus, with species assignments trustworthy only in particular groups. Where species matters, full-length sequencing or shotgun metagenomics is the honest route.

Why do I need blanks on a 16S run?

Because extraction kits and reagents carry bacterial DNA. On low biomass samples that background can be most of what is detected, and without sequenced blanks there is no way to separate it from the sample.

Does 16S tell me how much bacteria is present?

No. The data is compositional: it reports proportions within each sample, not absolute abundance. Pairing it with a quantitative measurement is the only way to make absolute statements.

16S or shotgun metagenomics?

16S for cheap, broad community profiling at genus level across many samples. Shotgun where species or strain resolution, functional content or non-bacterial members matter, at considerably higher cost per sample.

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