Bio Microbiome Qiime2 Workflow

Version Compatibility

Reference examples tested with: QIIME2 2026.1+ (amplicon distribution; framework now rachis), provenance-lib 2024.10+.

Before using code patterns, verify installed versions match. If versions differ:

• CLI: qiime --version, qiime info, then qiime <plugin> <action> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

The QIIME2 release tag (calendar-versioned YYYY.RELEASE, e.g. 2026.1) defines the plugin API AND the .qza artifact format; an sklearn taxonomy classifier .qza trained under one release may need retraining under another (the classifier is pinned to its scikit-learn version). The conda env name encodes the release and distribution (qiime2-amplicon-2026.1; renamed toward rachis-qiime2-<release> in 2026.4). Names and the install YAML URL are moving targets - verify the current release and distribution names against the live install page before pinning anything.

QIIME2 Amplicon Workflow

"Run my amplicon study through QIIME2" -> Move data through the framework as typed, provenance-carrying artifacts and defer every scientific choice to the owning skill - because a .qza is not a file, it is data plus its entire executable history, and that history is the deliverable.

• CLI: qiime tools import, qiime <plugin> <action> --i-* --p-* --m-* --o-*, qiime tools peek/export

Scope: the artifact/provenance/type machinery and the import/export/metadata/interface mechanics - this is the GLUE skill. Denoising params (trunc/trim/maxEE, DADA2 vs Deblur) -> amplicon-processing. Classifier/DB choice + training -> taxonomy-assignment. Diversity metric/sampling-depth/rarefaction + PERMANOVA-vs-dispersion -> diversity-analysis. DA tool choice/consensus -> differential-abundance. PICRUSt2 -> functional-prediction. Shotgun reads (moshpit distribution, Kraken2/MetaPhlAn/HUMAnN) -> metagenomics. This skill shows each scientific action and routes the decision out; it does not re-teach the method.

The Single Most Important Modern Insight -- A .qza Is Data Plus Its Executable History, Not a File Format

A .qza carries the data AND the complete computational graph that produced it. The semantic-type system plus the embedded provenance ARE the reproducibility guarantee - the whole reason to work inside the framework instead of passing loose BIOM/FASTA/Newick files. The cost is exact and unavoidable: there is no cat-ing the data. Three corollaries each common misuse violates:

1. Working THROUGH the framework keeps the chain; exporting breaks it. qiime tools export writes native data and silently drops the QIIME2 wrapper AND the provenance. Export early and go ad-hoc, and the final figure has no history back to the raw reads - the framework overhead was paid and the deliverable thrown away. Export at the LAST step, or use qiime2R::qza_to_phyloseq so the chain survives as far as possible.
2. Semantic types are a type system for biology. core-metrics-phylogenetic refuses a FeatureData[Taxonomy] where a FeatureTable[Frequency] belongs, BEFORE running. A type error is the guard WORKING - fix the upstream action that made the wrong type, do not launder it by re-importing.
3. A .qzv is terminal and a classifier is version-pinned. A Visualizer's output can never be another action's input (keep the .qza it was made from). An sklearn classifier .qza trained under 2024.x raises a version-mismatch under 2026.x - the training version is part of the method.

Organize the analysis around protecting the provenance chain and the type contract, not around listing flags.

What Is Inside a .qza

A .qza (QIIME Zipped Artifact) and .qzv (Visualization) are ZIP archives keyed at top level by a UUID. Every artifact carries four things:

1. UUID - identifies THIS computation (provenance references inputs/outputs by UUID), not just a file.
2. Semantic TYPE - what the data MEANS: FeatureTable[Frequency], SampleData[PairedEndSequencesWithQuality], Phylogeny[Rooted], FeatureData[Taxonomy], FeatureData[Sequence], DistanceMatrix, SampleData[AlphaDiversity]. Types can carry Properties (SampleData[AlphaDiversity] % Properties('phylogenetic')).
3. FORMAT - the on-disk layout the bytes live in (e.g. BIOMV210DirFmt, a Newick file). Type is the meaning; format is the bytes.
4. PROVENANCE - in a provenance/ subtree: for every upstream action the plugin/action name, every parameter value, input/output UUIDs, plugin + framework versions, execution environment, timestamp, and BibTeX citations. The references form a DAG of the whole analysis.

qiime tools peek table.qza                    # UUID + Type + Format, without unzipping
qiime tools validate table.qza --level max    # archive integrity + payload conforms to its format
qiime tools extract --input-path table.qza --output-path extracted/   # FULL archive incl provenance (read by hand)
qiime tools export  --input-path table.qza --output-path exported/     # ONLY the native data - DROPS provenance

qiime tools extract keeps the QIIME2 structure (data + provenance/); qiime tools export is the one-way door out. A plain unzip table.qza works too (it is a standard ZIP).

Tool / Interface Taxonomy

Neither q2cli nor the Artifact API is "more reproducible" - provenance is identical; pick by host environment. An Action is a Method (Artifacts in -> Artifacts out), a Visualizer (-> exactly one terminal .qzv), or a Pipeline (-> many Artifacts and/or Visualizations, e.g. core-metrics-phylogenetic). The Method/Visualizer distinction is WHY a .qzv is a dead end. The legacy q2studio desktop GUI is dead (last release 2022.8); the no-CLI answers are Galaxy + view.qiime2.org.

Decision Tree by Scenario

Import

Goal: Turn raw demultiplexed reads into a typed, provenance-rooted artifact with the correct Phred offset.

Approach: Write a V2 manifest (TSV, absolute paths), declare the semantic type and the format whose name encodes the Phred offset, then immediately summarize to confirm the reads decoded sanely.

# manifest.tsv (TAB-separated, V2; absolute paths):
#   sample-id<TAB>forward-absolute-filepath<TAB>reverse-absolute-filepath
qiime tools import \
    --type 'SampleData[PairedEndSequencesWithQuality]' \
    --input-path manifest.tsv \
    --input-format PairedEndFastqManifestPhred33V2 \
    --output-path demux.qza
# Phred offset is BAKED INTO the format name: Phred33V2 (modern Illumina) vs Phred64V2 (legacy).
# V1 was CSV with a `direction` column; V2 is TSV with separate forward/reverse columns - prefer V2.

qiime demux summarize --i-data demux.qza --o-visualization demux.qzv   # per-base quality (drives trunc choices)

For EMP-multiplexed data: import --type 'EMPPairedEndSequences', then qiime demux emp-paired --i-seqs emp.qza --m-barcodes-file metadata.tsv --m-barcodes-column barcode-sequence --o-per-sample-sequences demux.qza --o-error-correction-details ec.qza. The per-base quality plot in demux.qzv is read by amplicon-processing to pick truncation - not here.

The Orchestration Skeleton (each science step DEFERS)

The pipeline shape, with every method choice routed to its owning skill:

# Denoise -> ASV table + rep-seqs.  PARAM CHOICE (trunc/trim/maxEE, DADA2 vs Deblur) -> amplicon-processing
qiime dada2 denoise-paired --i-demultiplexed-seqs demux.qza \
    --p-trunc-len-f 0 --p-trunc-len-r 0 \
    --o-table table.qza --o-representative-sequences rep-seqs.qza --o-denoising-stats stats.qza

qiime tools peek table.qza    # confirm Type is FeatureTable[Frequency] before wiring downstream

# Taxonomy.  CLASSIFIER + DB choice and training -> taxonomy-assignment
# Use a classifier .qza trained for THIS release (data.qiime2.org/<release>/common/...); old ones break.
qiime feature-classifier classify-sklearn \
    --i-classifier classifier.qza --i-reads rep-seqs.qza --o-classification taxonomy.qza

# Phylogeny (Pipeline) -> rooted tree for UniFrac/Faith PD
qiime phylogeny align-to-tree-mafft-fasttree --i-sequences rep-seqs.qza \
    --o-alignment aln.qza --o-masked-alignment masked-aln.qza \
    --o-tree unrooted-tree.qza --o-rooted-tree rooted-tree.qza

# Diversity (Pipeline).  SAMPLING DEPTH + metric + rarefy-or-not -> diversity-analysis (pick depth from alpha-rarefaction)
qiime diversity core-metrics-phylogenetic --i-phylogeny rooted-tree.qza --i-table table.qza \
    --p-sampling-depth 10000 --m-metadata-file metadata.tsv --output-dir core-metrics/
# PERMANOVA via diversity beta-group-significance; the location-vs-dispersion (betadisper) confound -> diversity-analysis

# Differential abundance - MODERN q2-composition (NOT add-pseudocount+ancom).  Tool choice/consensus -> differential-abundance
qiime composition ancombc --i-table table.qza --m-metadata-file metadata.tsv \
    --p-formula 'group' --o-differentials ancombc.qza
qiime composition da-barplot --i-data ancombc.qza --o-visualization ancombc-barplot.qzv

core-metrics-phylogenetic and align-to-tree-mafft-fasttree are Pipelines (one call, a directory of artifacts + Emperor .qzvs out). --p-formula takes column names from the Metadata; annotate integer ID/batch columns categorical (below) or they enter the model as continuous covariates.

Metadata

The Metadata TSV is the spine - the same --m-metadata-file drives demux barcodes, group-significance, taxa barplots, ANCOM-BC grouping, and Emperor coloring. First column header is the ID column (sample-id, id, #SampleID, ...). An optional second row #q2:types overrides type inference per column (categorical / numeric):

sample-id	subject	group
#q2:types	categorical	categorical
s1	101	treatment
s2	102	control

Without the #q2:types row, a column of only integers is inferred numeric - so a subject/batch/timepoint ID silently becomes a continuous covariate. Annotate ID-like integer columns categorical. Validate the sheet with Keemei (Rideout 2016 GigaScience 5:27) before running - a malformed metadata file is a top cause of cryptic action failures. qiime metadata tabulate --m-input-file metadata.tsv --o-visualization metadata.qzv renders any metadata (including an artifact viewed as metadata, e.g. taxonomy or denoising stats) as a table.

Provenance Replay

Goal: Recover the executable commands that produced an artifact, from the artifact alone.

Approach: Parse the embedded provenance DAG and regenerate a q2cli (or Artifact-API) script plus a citations BibTeX.

qiime tools replay-provenance --in-fp core-metrics/ --out-fp replay.sh --usage-driver cli
qiime tools replay-citations  --in-fp core-metrics/ --out-fp citations.bib
# --usage-driver selects cli vs python3/artifact-api output. Verify flag spelling with
# `qiime tools replay-provenance --help` on the installed build (the interface is still maturing).

Replay recovers the commands; it is not a guaranteed bit-identical rerun across very different releases (plugin versions are part of the record). The aggregated DAG citations are how a methods section's references come straight from provenance, also via the .qzv Citations tab on view.qiime2.org.

Export (the one-way door)

Goal: Hand the data to R/Python when the analysis is no longer expressible in QIIME2 - while losing as little provenance as possible.

Approach: Stay in artifacts as long as the work is QIIME2-expressible; export (or read into phyloseq) only at the last step, and keep the upstream .qzas so the chain survives up to the exit.

qiime tools export --input-path table.qza --output-path exported/      # -> exported/feature-table.biom
biom convert -i exported/feature-table.biom -o feature-table.tsv --to-tsv
# FeatureData[Sequence] -> dna-sequences.fasta; FeatureData[Taxonomy] -> taxonomy.tsv; Phylogeny[Rooted] -> tree.nwk

Export DROPS the QIIME2 wrapper and the provenance - the exported TSV has no history back to the reads. For R, prefer qiime2R::qza_to_phyloseq('table.qza', 'taxonomy.qza', 'rooted-tree.qza', 'metadata.tsv') (Bisanz), which reads artifacts directly and assembles a phyloseq object without manual export. Record where the chain ends.

Per-Method Failure Modes

Export-early-loses-provenance

Trigger: qiime tools export to TSV at step three, then everything else in a notebook. Mechanism: export writes native data only and drops the provenance/ subtree. Symptom: the final figure has no provenance back to the raw reads - the framework overhead bought nothing. Fix: export at the LAST step; save upstream .qzas; or use qiime2R::qza_to_phyloseq so the chain survives to the exit.

Semantic-type mismatch treated as a bug

Trigger: feeding a Phylogeny[Unrooted] or a FeatureData[Taxonomy] where a FeatureTable[Frequency] is required. Mechanism: the type system refuses incompatible inputs at the interface boundary before running. Symptom: "expected an artifact of type ..." error. Fix: this is the guard WORKING; qiime tools peek to read the actual Type, then fix the UPSTREAM action that produced the wrong type - do not re-import to coerce it.

Classifier / artifact version break across releases

Trigger: a silva-138-99-nb-classifier.qza from 2024.x used under 2026.x. Mechanism: the sklearn naive-Bayes classifier is pinned to its scikit-learn version; provenance replay assumes recorded plugin versions. Symptom: scikit-learn version-mismatch warning/error, or refusal to load. Fix: download/train the classifier for YOUR release (the data.qiime2.org/<release>/common/... URLs are release-namespaced); retrain or pin the whole env if reusing an old one.

Manifest Phred / format error on import

Trigger: Phred64V2 on modern Illumina, V1-vs-V2 manifest confusion, relative paths, or SampleData[...] for still-multiplexed EMP data. Mechanism: the Phred offset is baked into the format name and is applied without checking. Symptom: silently mis-decoded quality scores, or an import that "works" but demux summarize shows garbage qualities. Fix: modern Illumina = Phred33; V2 TSV manifests with absolute paths; qiime demux summarize immediately after import; EMP data needs EMPPairedEndSequences + qiime demux.

A .qzv treated as data

Trigger: trying to feed a .qzv into the next action. Mechanism: a Visualizer's output is terminal by the framework's type contract. Symptom: the action will not accept it as an input. Fix: keep and feed the .qza the Visualizer was MADE from; a .qzv is for viewing only (browser or view.qiime2.org).

Metadata numeric cast

Trigger: an integer subject/batch/timepoint column with no #q2:types row. Mechanism: inference casts an all-integer column to numeric. Symptom: an ID enters a model as a continuous covariate; nonsensical group results. Fix: add a #q2:types row annotating ID-like columns categorical; validate with Keemei.

Mixing distributions

Trigger: expecting amplicon plugins in moshpit, or shotgun assembly in amplicon. Mechanism: distributions are curated, partially-disjoint plugin sets. Symptom: "plugin not found." Fix: amplicon/marker-gene -> amplicon distribution (renamed qiime2 in 2026.4); shotgun -> moshpit (and -> metagenomics); pin both distribution and release.

Quantitative Thresholds

Most scientific magic numbers live in the five sibling skills, not here - this skill owns the machinery.

Common Errors

References

• Bolyen E, Rideout JR, Dillon MR, Bokulich NA, ..., Caporaso JG. 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37:852-857.
• Keefe CR, Dillon MR, Gehret E, Herman C, Jewell M, Wood CV, Bolyen E, Caporaso JG. 2023. Facilitating bioinformatics reproducibility with QIIME 2 Provenance Replay. PLoS Comput Biol 19(11):e1011676.
• Lin H, Peddada SD. 2020. Analysis of compositions of microbiomes with bias correction. Nat Commun 11:3514.
• Rideout JR, Chase JH, Bolyen E, Ackermann G, Gonzalez A, Knight R, Caporaso JG. 2016. Keemei: cloud-based validation of tabular bioinformatics file formats in Google Sheets. GigaScience 5:27.
• Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, Huttley GA, Caporaso JG. 2018. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin. Microbiome 6:90.

Related Skills

• amplicon-processing - DADA2 denoising parameters this skill defers (trunc/trim/maxEE, DADA2 vs Deblur)
• taxonomy-assignment - Classifier and reference-database choice and training behind classify-sklearn
• diversity-analysis - Sampling depth, diversity metric, rarefaction, and the PERMANOVA-vs-dispersion confound
• differential-abundance - DA tool choice and consensus behind composition ancombc
• functional-prediction - PICRUSt2 functional prediction from the feature table
• metagenomics/kraken-classification - Shotgun (moshpit distribution) read classification, not amplicon
• phylogenetics/tree-io - Phylogenetic tree I/O for UniFrac / Faith PD
• read-qc/adapter-trimming - cutadapt primer removal before import/denoising
• workflows/microbiome-pipeline - End-to-end amplicon pipeline