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.
• Match classifier reference database, release, primer region, orientation and QIIME/scikit-learn compatibility. Record its source URL, database version and checksum. 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

Use actual tab characters, absolute paths visible to the runtime, and one row per sample. 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.

Execute

The helper lives at scripts/amplicon_workflow.py. 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:

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 region-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.

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; merging loss suggests overlap or orientation; 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.

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.