Process paired-end 16S sequencing reads in QIIME 2 to produce ASVs, distinct sequence variants, and taxonomic assignments. Check input files, primer settings, and read retention while preserving artifacts that record the processing history.
Processes paired-end 16S amplicon reads into QIIME 2 ASVs and taxonomy with retained artifact provenance. Checks paired FASTQ manifests, primer orientation diagnostics, predicted post-trimming overlap, sample IDs, runtime versions and read retention, and guides selection of compatible taxonomic classifiers.
Run score Not tested
See how to installThis skill processes paired-end 16S reads in QIIME 2 to produce ASVs, which distinguish sequence variants, and taxonomic assignments. It checks FASTQ pairs and sample IDs, removes primers with Cutadapt, denoises reads with DADA2, and classifies ASVs using a specified classifier. It tracks the number of reads retained at each stage and saves .qza and .qzv artifacts with processing history, along with execution records.
Use it to process sample-level 16S data and check for input mismatches or read loss. It is also useful when you need results that can be traced back through the workflow.
manifest.tsv, the sample metadata, and the primer settings.”Validation requires Python 3.10 or later. Execution requires the official conda or container distribution of QIIME 2 2026.7, its required plugins, and a compatible, trusted classifier. The bundled runner assumes primers remain at the 5′ ends of input reads. Read counts are not absolute cell counts, and taxonomy assignments do not identify strains. Diversity analysis and automatic selection of rarefaction depth are outside this skill’s scope.
This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.
AI checked the SKILL.md for operations that need care and for setup requirements. The score comes only from actually running the skill (hands-on test).
Not counted in the score. Rated by AI (OpenAI Codex) as of Oct 24, 2026. Treat it as a guide and read the original before installing.
Review the instructions the agent will receive before you install.
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.
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.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.
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.
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.
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.
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