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11. 16S / 18S / ITS Marker Gene ASV Inference & Diversity

⚡ Pipeline 11 of 16  ·  🧬 Microbiome & 16S

Exact Amplicon Sequence Variant (ASV) resolution with DADA2, error model modeling, taxonomic classification against SILVA 138, and phylogenetic alpha/beta diversity quantification.

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Microbiome • 16S/18S Authority: QIIME 2 / DADA2 / SILVA
Production Blueprint v4.5

11. 16S / 18S / ITS Marker Gene ASV Inference & Diversity

Exact Amplicon Sequence Variant (ASV) resolution with DADA2, error model modeling, taxonomic classification against SILVA 138, and phylogenetic alpha/beta diversity quantification.

Metric / RequirementSpecificationTarget Configuration
Core Engine / FrameworkQIIME 2 (2024.5) + DADA2 + SILVA 138Nextflow / Snakemake Compatible
Benchmark Runtime~2 – 3 hours per 96-well run16 CPU cores / 32 GB RAM
Standard Containerquay.io/qiime2/amplicon:2024.5Docker • Singularity • Conda
Input → DeliverableDemultiplexed Amplicon FASTQs → ASV Table, Taxonomy Barplots, PCoA Plots
Workflow Chain: Demux FASTQs → cutadapt (primer trim) → qiime dada2 denoise-paired → qiime feature-classifier (SILVA 138) → qiime diversity core-metrics
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Step 1: Import Demultiplexed FASTQs into QIIME 2 Artifact (.qza)

QIIME 2 tools import

Ingests paired-end FASTQs and manifest into compressed QIIME 2 semantic artifacts.

qiime tools import \
  --type 'SampleData[PairedEndSequencesWithQuality]' \
  --input-path manifest.tsv \
  --output-path demux_paired.qza \
  --input-format PairedEndFastqManifestPhred33V2
Key Flags & Notes: Semantic artifacts encapsulate provenance tracking for all downstream operations.
⚡

Step 2: Exact Amplicon Sequence Variant (ASV) Denoising with DADA2

qiime dada2 denoise-paired

Learns sequencing error models to infer true biological sequences at single-nucleotide resolution.

qiime dada2 denoise-paired \
  --i-demultiplexed-seqs demux_paired.qza \
  --p-trim-left-f 17 --p-trim-left-r 21 \
  --p-trunc-len-f 250 --p-trunc-len-r 220 \
  --o-table table.qza \
  --o-representative-sequences rep-seqs.qza \
  --o-denoising-stats denoising-stats.qza \
  --p-n-threads 16
Key Flags & Notes: --p-trim-left removes variable-length primer sequences; resolves ASVs without arbitrary 97% OTU clustering.
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Step 3: Taxonomic Classification with Naive Bayes & SILVA 138

qiime feature-classifier

Classifies ASVs against curated SILVA 138 full-length 16S reference database.

qiime feature-classifier classify-sklearn \
  --i-classifier /ref/silva-138-99-nb-classifier.qza \
  --i-reads rep-seqs.qza \
  --o-classification taxonomy.qza \
  --p-n-jobs 8
Key Flags & Notes: Assigns Kingdom down to Species taxonomic confidence scores.
⚡

Step 4: Phylogenetic Diversity & UniFrac PCoA Analysis

qiime diversity core-metrics-phylogenetic

Generates rooted phylogenetic trees and computes Shannon diversity, Faith PD, Bray-Curtis, and Weighted UniFrac.

# Build tree
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 diversity suite
qiime diversity core-metrics-phylogenetic \
  --i-phylogeny rooted-tree.qza \
  --i-table table.qza \
  --p-sampling-depth 10000 \
  --m-metadata-file sample-metadata.tsv \
  --output-dir core-metrics-results/
Key Flags & Notes: --p-sampling-depth rarifies samples to uniform depth for unbiased beta diversity comparison.

🚀 Automated Nextflow (nf-core) Production Execution

Execute this complete workflow across any SLURM, AWS Batch, or Google Cloud HPC cluster using the verified community container pipeline:

nextflow run nf-core/ampliseq -r 2.11.0 \
  --input samplesheet.tsv \
  --FW_primer GTGYCAGCMGCCGCGGTAA \
  --RV_primer GGACTACNVGGGTWTCTAAT \
  --outdir ./results_amplicon/ \
  -profile docker
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