Automated summary
Provides a QIIME2 command-line sequence for 16S/ITS amplicon analysis, from FASTQ import through alpha and beta diversity analyses.
Strengths
- Covers the full pipeline: import, DADA2 or Deblur denoising, taxonomy assignment, phylogenetic tree, and diversity metrics
- Offers two alternative denoisers (DADA2 and Deblur) with explicit truncation and quality-filtering parameters
- Relies on QIIME2 .qza/.qzv artifacts, giving reproducible provenance for each step
- Includes visualization commands (demux summarize, taxa barplot, significance tests)
Limitations
- Assumes QIIME2 is already installed and the user understands manifests, artifacts, and truncation settings
- Truncation lengths and sampling depth (10000) are illustrative values that must be adapted to the actual dataset
- The SKILL.md is cut off after the beta-group-significance example, so the end of the workflow is not detailed
Use it when you prefer a command-line amplicon analysis inside the QIIME2 ecosystem over an R DADA2/phyloseq workflow.
Avoid it if you lack a working QIIME2 installation or need the advanced statistical tooling specific to R/phyloseq.
Security analysis
SafeThe skill contains standard QIIME2 bioinformatics commands for importing FASTQ data, denoising, taxonomy assignment, phylogeny, diversity, and differential abundance. It does not instruct destructive filesystem operations, secret exfiltration, obfuscated payload execution, or safety disabling. Although it declares run_shell_command, all shell usage is legitimate domain-specific analysis.
No concerns found
How this analysis is madeExamples
I have paired-end 16S FASTQ files and a manifest.tsv. Run a QIIME2 workflow: import the data, denoise with DADA2, assign taxonomy with the SILVA 138 classifier, and build a rooted tree.Using my QIIME2 table.qza and rooted-tree.qza, compute core phylogenetic metrics at a sampling depth of 10000 and test Shannon diversity significance across my Group metadata column.Instead of DADA2, denoise my demultiplexed QIIME2 data with Deblur at trim length 250 after quality filtering, and give me the resulting table and representative sequences.name: bio-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 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
QIIME2 Amplicon Workflow
Import Data
# 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
# 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
# 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
# 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
# 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
# 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)
# 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
# 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
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