Analyze 16S or ITS amplicon data, sequences from amplified marker regions, through the QIIME2 command line. Import reads, remove noise, assign taxonomy, compare microbial diversity and abundance between groups, and retain a record of processing steps.
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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.
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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:
<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"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.
qiime dada2 denoise-paired, qiime diversity core-metrics-phylogenetic# 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
# 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
# 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
# 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
# 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
# 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
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.
# 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 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.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.tsv (tab-separated)
sample-id Group Timepoint
sample1 Treatment Day0
sample2 Control Day0
sample3 Treatment Day7
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中文简介正在准备中。当前显示英语简介。
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中文简介正在准备中。当前显示英语简介。
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中文简介正在准备中。当前显示英语简介。
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中文简介正在准备中。当前显示英语简介。
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中文简介正在准备中。当前显示英语简介。