---
name: microbiome-qiime2-workflow
description: QIIME2 command-line workflow for 16S/ITS amplicon analysis. Alternative to DADA2/phyloseq R workflow with built-in provenance tracking. Use when preferring CLI over R, needing reproducible provenance, or working within QIIME2 ecosystem.
tool_type: cli
primary_tool: qiime2
---
## Version Compatibility
Reference examples tested with: DADA2 1.30+, MAFFT 7.520+, QIIME2 2024.2+, phyloseq 1.46+, scanpy 1.10+, scikit-learn 1.4+
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --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.
# QIIME2 Amplicon Workflow
**"Run my amplicon analysis through QIIME2"** -> Process 16S/ITS amplicon data end-to-end using the QIIME2 CLI with built-in provenance tracking, from import through denoising, taxonomy, and diversity analysis.
- CLI: `qiime dada2 denoise-paired`, `qiime diversity core-metrics-phylogenetic`
## Import Data
```bash
# Import paired-end FASTQ with manifest
qiime tools import \
--type 'SampleData[PairedEndSequencesWithQuality]' \
--input-path manifest.tsv \
--output-path demux.qza \
--input-format PairedEndFastqManifestPhred33V2
# View demultiplexed summary
qiime demux summarize \
--i-data demux.qza \
--o-visualization demux.qzv
```
## Denoise with DADA2
```bash
# DADA2 denoising (creates ASV table + representative sequences)
qiime dada2 denoise-paired \
--i-demultiplexed-seqs demux.qza \
--p-trunc-len-f 240 \
--p-trunc-len-r 160 \
--p-trim-left-f 0 \
--p-trim-left-r 0 \
--p-max-ee-f 2 \
--p-max-ee-r 2 \
--p-n-threads 0 \
--o-table table.qza \
--o-representative-sequences rep-seqs.qza \
--o-denoising-stats denoising-stats.qza
# View denoising stats
qiime metadata tabulate \
--m-input-file denoising-stats.qza \
--o-visualization denoising-stats.qzv
```
## Alternative: Deblur Denoising
```bash
# Quality filter first
qiime quality-filter q-score \
--i-demux demux.qza \
--o-filtered-sequences demux-filtered.qza \
--o-filter-stats filter-stats.qza
# Deblur denoise
qiime deblur denoise-16S \
--i-demultiplexed-seqs demux-filtered.qza \
--p-trim-length 250 \
--p-sample-stats \
--o-representative-sequences rep-seqs-deblur.qza \
--o-table table-deblur.qza \
--o-stats deblur-stats.qza
```
## Taxonomy Assignment
```bash
# Download pre-trained classifier (SILVA 138)
# wget https://data.qiime2.org/2024.5/common/silva-138-99-nb-classifier.qza
# Classify with sklearn naive Bayes
qiime feature-classifier classify-sklearn \
--i-classifier silva-138-99-nb-classifier.qza \
--i-reads rep-seqs.qza \
--o-classification taxonomy.qza
# Visualize taxonomy
qiime metadata tabulate \
--m-input-file taxonomy.qza \
--o-visualization taxonomy.qzv
# Taxonomic barplot
qiime taxa barplot \
--i-table table.qza \
--i-taxonomy taxonomy.qza \
--m-metadata-file metadata.tsv \
--o-visualization taxa-barplot.qzv
```
## Phylogenetic Tree
```bash
# Build phylogeny with MAFFT + FastTree
qiime phylogeny align-to-tree-mafft-fasttree \
--i-sequences rep-seqs.qza \
--o-alignment aligned-rep-seqs.qza \
--o-masked-alignment masked-aligned-rep-seqs.qza \
--o-tree unrooted-tree.qza \
--o-rooted-tree rooted-tree.qza
```
## Diversity Analysis
```bash
# Core metrics (alpha + beta diversity)
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-results
# Alpha diversity significance
qiime diversity alpha-group-significance \
--i-alpha-diversity core-metrics-results/shannon_vector.qza \
--m-metadata-file metadata.tsv \
--o-visualization shannon-significance.qzv
# Beta diversity significance (PERMANOVA)
qiime diversity beta-group-significance \
--i-distance-matrix core-metrics-results/weighted_unifrac_distance_matrix.qza \
--m-metadata-file metadata.tsv \
--m-metadata-column Group \
--p-method permanova \
--o-visualization weighted-unifrac-permanova.qzv
```
## Differential Abundance (ANCOM)
**Goal:** Identify taxa with significantly different abundances between groups using QIIME2's ANCOM implementation.
**Approach:** Collapse the feature table to genus level, add pseudocounts for log-ratio computation, and run ANCOM to test for differential abundance per taxon.
```bash
# Collapse to genus level
qiime taxa collapse \
--i-table table.qza \
--i-taxonomy taxonomy.qza \
--p-level 6 \
--o-collapsed-table table-l6.qza
# Add pseudocount and run ANCOM
qiime composition add-pseudocount \
--i-table table-l6.qza \
--o-composition-table comp-table-l6.qza
qiime composition ancom \
--i-table comp-table-l6.qza \
--m-metadata-file metadata.tsv \
--m-metadata-column Group \
--o-visualization ancom-l6.qzv
```
## Export to R/Python
```bash
# Export feature table to BIOM
qiime tools export \
--input-path table.qza \
--output-path exported
# Convert BIOM to TSV
biom convert \
-i exported/feature-table.biom \
-o feature-table.tsv \
--to-tsv
# Export taxonomy
qiime tools export \
--input-path taxonomy.qza \
--output-path exported
# Export tree
qiime tools export \
--input-path rooted-tree.qza \
--output-path exported
```
## Manifest File Format
```
# manifest.tsv (tab-separated)
sample-id forward-absolute-filepath reverse-absolute-filepath
sample1 /path/to/sample1_R1.fastq.gz /path/to/sample1_R2.fastq.gz
sample2 /path/to/sample2_R1.fastq.gz /path/to/sample2_R2.fastq.gz
```
## Metadata File Format
```
# metadata.tsv (tab-separated)
sample-id Group Timepoint
sample1 Treatment Day0
sample2 Control Day0
sample3 Treatment Day7
```
## Related Skills
- amplicon-processing - DADA2 R workflow alternative
- taxonomy-assignment - More database options
- diversity-analysis - phyloseq R alternative
- differential-abundance - ALDEx2/ANCOM-BC in R