QIIME 2 paired-end 16S amplicons

Use for a demultiplexed, paired-end 16S assay with known primers, quality encoding, expected insert length, and sample metadata. This bounded workflow imports reads, removes 5′ primers, denoises with DADA2, classifies ASVs using an explicitly supplied classifier, and retains .qza/.qzv provenance. Do not treat read counts as absolute cell counts or taxonomy assignments as strain identification.

Establish the assay before running

• Confirm Phred+33, read orientation, primer sequences as sequenced in forward/reverse reads, and whether primers have already been removed. The bundled runner requires primers still present at the 5′ ends; it anchors Cutadapt matching and discards untrimmed pairs. Already trimmed reads need a direct import → DADA2 workflow with that omission recorded in provenance.
• Choose truncation positions from actual per-base quality and error profiles. trunc-f/r are positions after primer removal. The expected maximum insert length also excludes primers. Require trunc_f + trunc_r - maximum_insert_length >= 12; use a margin for length variation. The check predicts geometrical overlap, not successful biological merging.
• Choose a classifier whose reference database, taxonomic coverage, orientation and training approach fit the assay. Full-length classifiers are supported; primer-region-specific training is not mandatory. Match its scikit-learn version exactly to the installed environment (the official 2026.7 distribution pins 1.7.1). Record source URL, database version and checksum. QIIME's current data-resources page links externally hosted classifiers for 2026.4 and later; its older downloads are not automatically compatible. Do not automatically fetch an arbitrary “latest” classifier or reuse an incompatible serialized sklearn model. Load sklearn classifier artifacts only from trusted sources: QZA format validation does not make an untrusted serialized model safe.
• Include extraction blanks, PCR negatives, and a mock community where available. The runner rejects empty FASTQs; preserve empty-control IDs separately and report them rather than silently deleting control evidence. Assess contamination before ecological interpretation.

Input files

Manifest is a tab-separated PairedEndFastqManifestPhred33V2 file with exactly these headers:

sample-id	forward-absolute-filepath	reverse-absolute-filepath
sample1	/data/sample1_R1.fastq.gz	/data/sample1_R2.fastq.gz

This is the helper's deliberately narrow manifest profile. Use actual tab characters, literal absolute paths visible to the runtime (expand environment variables before calling this helper), and one row per sample; no comment/directive rows or additional columns in this manifest. Do not reverse-complement R2 files. Sample metadata is a tab-separated file with first column sample-id, unique IDs matching the manifest, and optional #q2:types annotation. Include covariates and biological replicate IDs needed downstream. The helper checks metadata IDs and row structure; QIIME performs full metadata typing/directive validation during execution. Metadata used by actions persists in artifact provenance, so use de-identified biological replicate IDs.

Execute

The helper lives at scripts/amplicon_workflow.py. Commands below assume the skill directory is the working directory. First validate without QIIME. These example primers and lengths are illustrative, not universal assay settings:

python scripts/amplicon_workflow.py validate \
  --manifest manifest.tsv --metadata sample-metadata.tsv \
  --primer-f GTGYCAGCMGCCGCGGTAA --primer-r GGACTACNVGGGTWTCTAAT \
  --trunc-f 220 --trunc-r 200 --amplicon-max 300

Then run in the QIIME 2 2026.7 environment with a compatible classifier. This study-specific invocation is illustrative; choose lengths using a preceding quality inspection or pilot:

python scripts/amplicon_workflow.py run \
  --manifest manifest.tsv --metadata sample-metadata.tsv \
  --primer-f GTGYCAGCMGCCGCGGTAA --primer-r GGACTACNVGGGTWTCTAAT \
  --trunc-f 220 --trunc-r 200 --amplicon-max 300 \
  --classifier compatible-classifier.qza --threads 4 --output run01

run executes immediately, writes only to a fresh output directory, and stops on a failing QIIME command. It streams through every paired FASTQ record to catch mismatched IDs/order/counts, checks sequence/quality consistency and sample alignment, and profiles the first 1,000 read pairs for exact IUPAC primer matches. Low exact-match rates are warnings: Cutadapt allows mismatches, but a low rate can also indicate incorrect orientation, adapters, or already-trimmed data. At least one complete pair per sample must meet nominal post-primer truncation lengths. These length estimates subtract the stated primer lengths; they do not simulate Cutadapt indels or quality filtering, and do not establish that any pair will actually merge.

The runner uses --output-dir for plugin methods with evolving output sets, preserving Cutadapt statistics and DADA2 base-transition artifacts when supplied by the release. The 2026.7 table summary also produces feature-frequencies.qza and sample-frequencies.qza beside table.qzv. It records qiime-info.txt, commands.json, workflow.log, input QC, classifier checksum and output artifact checksums. It runs maximum-level QIIME artifact validation before reporting completion. See references/runtime-and-interpretation.md for the release-pinned runtime, actual validation scope, restart handling and scientific interpretation.

Inspect results before analysis

Open trimmed.qzv, table.qzv, and taxa.qzv in a local QIIME visualization environment or QIIME 2 View as appropriate for the data. Examine quality/length profiles, per-sample depth and dominant taxa. Retain original artifacts rather than replacing them with CSV/BIOM exports: exports do not retain the original provenance graph.

retention-qc.json compares raw pairs with DADA2 input and non-chimeric reads, so trimming losses remain visible. A <50% retained fraction is a review heuristic, not a universal rejection rule. Inspect the individual stages in stats/stats.tsv: filtering loss suggests quality/expected-error settings; loss after forward denoising includes reverse-denoising and merging failures; chimera loss warrants reviewing library quality and parameters. Investigate missing/zero samples and control behavior before rarefaction, diversity, or differential abundance. Those downstream analyses need a separate design decision; this skill does not choose a rarefaction depth automatically. The standalone retention stats.tsv subcommand knows only DADA2 input counts: it reports raw_pairs: null and names that denominator explicitly. It supports merged-only paired DADA2 statistics, as produced by this runner; it rejects retained-unmerged/concatenated-read statistics.

Primary references

• Current installation entry point and amplicon documentation.
• Import formats.
• Cutadapt actions and DADA2 actions.
• Classifier data resources.

The rolling documentation may describe a development release. Inspect qiime info and action --help in the exact installed environment before adapting the pinned runner to a later release.