Bioinformatics Pipeline (QIIME 2 / DADA2)

SOP-L705 Revision 0.1

Author

Marc Los Huertos

EA Program, Pomona College

Published

2026-06-26

1 Purpose

This SOP describes the bioinformatics workflow for processing amplicon sequencing data (16S rRNA, ITS, 18S rRNA) from raw demultiplexed FASTQ files through quality filtering, denoising, taxonomic classification, and generation of analysis-ready feature tables. The pipeline uses QIIME 2 as the primary framework with DADA2 as the denoising algorithm (Bolyen et al., 2019; Callahan et al., 2016). DADA2 resolves amplicon sequence variants (ASVs) at single-nucleotide resolution, replacing the older OTU-clustering approach with a more reproducible and sensitive method (Callahan et al., 2017).

2 Scope

Applies to: Processing of Illumina paired-end amplicon sequencing data (MiSeq, NextSeq) for 16S, ITS, and 18S marker genes generated from EA Program environmental samples
Does not apply to: Shotgun metagenomics; long-read sequencing (PacBio, Nanopore); transcriptomics (RNA-seq); ddPCR data analysis (see SOP-L702)
Location: EA Program lab computers or personal workstations with QIIME 2 installed; high-performance computing cluster if available

3 Responsibilities

Role	Responsibility
Lab PI	Approve pipeline parameters and taxonomy database
Trained Personnel	Run the pipeline per this SOP; document all parameters
New Students	Complete training before independent analysis

4 Definitions

ASV (Amplicon Sequence Variant): An exact DNA sequence resolved from amplicon sequencing data by denoising algorithms (DADA2, Deblur); a higher-resolution alternative to OTUs that retains single-nucleotide differences between sequences.
DADA2: A denoising algorithm that models sequencing error rates to distinguish true biological sequences from sequencing errors; produces ASVs directly from paired-end reads without clustering.
Demultiplexing: The computational step of assigning reads to their source sample based on index sequences; produces per-sample FASTQ files from a multiplexed sequencing run.
Feature table (OTU/ASV table): A matrix of counts where rows are samples, columns are ASVs or OTUs, and each cell contains the number of reads from that sample assigned to that sequence variant; the primary data object in QIIME 2.
FASTQ: A text-based file format storing DNA sequences and their per-base quality scores (Phred scores); the standard output format from Illumina sequencers; paired-end data produces R1 and R2 FASTQ files per sample.
OTU (Operational Taxonomic Unit): A cluster of sequences grouped by a similarity threshold (typically 97% identity); a coarser representation of diversity than ASVs; largely superseded by ASV-based approaches.
Phred quality score (Q score): A logarithmic measure of base-call accuracy: \(Q = -10 \log_{10}(P_{error})\); Q20 = 99% accuracy; Q30 = 99.9% accuracy; used to set quality-trimming thresholds in DADA2.
QIIME 2: An open-source bioinformatics platform for microbiome analysis; manages data as typed artifacts (.qza) with embedded provenance records; wraps DADA2, taxonomic classifiers, diversity analyses, and visualization tools.
Rarefaction: Subsampling each sample to the same number of reads to correct for unequal sequencing depth before diversity comparisons; samples below the rarefaction depth are excluded.
Taxonomic classifier: A trained machine-learning model (e.g., naive Bayes classifier trained on SILVA or UNITE) that assigns taxonomy to ASV sequences by comparing them to a reference database.

5 Software Requirements

Software	Version (minimum)	Notes
QIIME 2	2024.2 or later	Install via conda/mamba; see https://docs.qiime2.org
DADA2 (QIIME 2 plugin)	Bundled with QIIME 2	R package also available for standalone use
R and RStudio	R 4.3+	For downstream statistical analysis; see SOP-L801
phyloseq (R package)	1.44+	For importing QIIME 2 artifacts into R (McMurdie & Holmes, 2013)
FastQC	0.12+	Optional; for initial read quality inspection

Install QIIME 2 in a dedicated conda environment to avoid dependency conflicts:

# Example installation (check docs.qiime2.org for current commands)
conda env create -n qiime2-2024.2 \
  --file https://data.qiime2.org/distro/amplicon/qiime2-amplicon-2024.2-py39-linux-conda.yml
conda activate qiime2-2024.2

6 Procedure

6.1 Data Organization

Create a project directory with the following structure:

project_name/
├── raw_data/          # Demultiplexed FASTQ files from sequencing facility
├── qiime2/            # QIIME 2 artifacts and visualizations
├── results/           # Exported tables, trees, taxonomy
├── scripts/           # Shell and R scripts
└── metadata/          # Sample metadata TSV file

Prepare a sample metadata file (metadata.tsv) in tab-separated format. The first column must be sample-id matching the FASTQ filenames. Include all experimental variables (site, treatment, date, depth, replicate). Validate the file at https://keemei.qiime2.org/ (Rideout et al., 2014).

6.2 Import Raw Reads

Import demultiplexed paired-end FASTQ files into a QIIME 2 artifact:

qiime tools import \
  --type 'SampleData[PairedEndSequencesWithQuality]' \
  --input-path raw_data/ \
  --input-format CasavaOneEightSingleLanePerSampleDirFmt \
  --output-path qiime2/demux.qza

Visualize read quality:

qiime demux summarize \
  --i-data qiime2/demux.qza \
  --o-visualization qiime2/demux-summary.qzv

Open demux-summary.qzv at https://view.qiime2.org to inspect quality score profiles. Use these plots to determine truncation parameters for DADA2 (Section 5.3).

6.3 Denoising with DADA2

DADA2 performs quality filtering, chimera removal, and denoising in a single step, producing an ASV feature table and representative sequences (Callahan et al., 2016).

qiime dada2 denoise-paired \
  --i-demultiplexed-seqs qiime2/demux.qza \
  --p-trunc-len-f 240 \
  --p-trunc-len-r 200 \
  --p-trim-left-f 0 \
  --p-trim-left-r 0 \
  --p-n-threads 4 \
  --o-table qiime2/table.qza \
  --o-representative-sequences qiime2/rep-seqs.qza \
  --o-denoising-stats qiime2/dada2-stats.qza

Setting Truncation Parameters

Choose --p-trunc-len-f and --p-trunc-len-r based on the quality plots from Section 5.2. Truncate where the median quality score drops below 25–30. The forward and reverse reads must still overlap by at least 20 bp after truncation for successful merging. For 16S V4 (515F/806R, ~290 bp amplicon) with 2x250 reads, typical values are 240 (forward) and 200 (reverse).

Review denoising statistics:

qiime metadata tabulate \
  --m-input-file qiime2/dada2-stats.qza \
  --o-visualization qiime2/dada2-stats.qzv

Check the percentage of reads passing each filter step (input, filtered, denoised, merged, non-chimeric). A healthy run retains > 60% of input reads through the full pipeline.

6.4 Taxonomic Classification

Assign taxonomy to ASVs using a pre-trained Naive Bayes classifier. Download the appropriate classifier from the QIIME 2 Data Resources page (https://docs.qiime2.org/2024.2/data-resources/).

For 16S (SILVA 138.1):

qiime feature-classifier classify-sklearn \
  --i-classifier silva-138.1-ssu-nr99-515f-806r-classifier.qza \
  --i-reads qiime2/rep-seqs.qza \
  --o-classification qiime2/taxonomy.qza

For ITS (UNITE):

qiime feature-classifier classify-sklearn \
  --i-classifier unite-ver9-dynamic-all-29.11.2022-classifier.qza \
  --i-reads qiime2/rep-seqs.qza \
  --o-classification qiime2/taxonomy.qza

Visualize taxonomy:

qiime taxa barplot \
  --i-table qiime2/table.qza \
  --i-taxonomy qiime2/taxonomy.qza \
  --m-metadata-file metadata/metadata.tsv \
  --o-visualization qiime2/taxa-barplot.qzv

6.5 Phylogenetic Tree Construction

Build a rooted phylogenetic tree for diversity analyses:

qiime phylogeny align-to-tree-mafft-fasttree \
  --i-sequences qiime2/rep-seqs.qza \
  --o-alignment qiime2/aligned-rep-seqs.qza \
  --o-masked-alignment qiime2/masked-aligned-rep-seqs.qza \
  --o-tree qiime2/unrooted-tree.qza \
  --o-rooted-tree qiime2/rooted-tree.qza

6.6 Filtering

Remove contaminant ASVs identified in negative controls, mitochondrial and chloroplast sequences, and low-frequency ASVs:

# Remove mitochondria and chloroplast
qiime taxa filter-table \
  --i-table qiime2/table.qza \
  --i-taxonomy qiime2/taxonomy.qza \
  --p-exclude mitochondria,chloroplast \
  --o-filtered-table qiime2/table-filtered.qza

# Remove low-frequency ASVs (e.g., singletons)
qiime feature-table filter-features \
  --i-table qiime2/table-filtered.qza \
  --p-min-frequency 2 \
  --o-filtered-table qiime2/table-final.qza

For decontamination using negative control data, export the feature table to R and use the decontam package (Davis et al., 2018).

6.7 Export for R Analysis

Export QIIME 2 artifacts for downstream analysis in R with phyloseq (McMurdie & Holmes, 2013):

qiime tools export \
  --input-path qiime2/table-final.qza \
  --output-path results/

qiime tools export \
  --input-path qiime2/taxonomy.qza \
  --output-path results/

qiime tools export \
  --input-path qiime2/rooted-tree.qza \
  --output-path results/

Alternatively, import QIIME 2 artifacts directly into R using the qiime2R package.

7 Quality Control

Inspect the DADA2 denoising stats for every run. Flag samples with fewer than 1,000 reads after denoising.
Verify that negative controls (NTC, extraction blank) contain fewer than 100 reads. If controls contain substantial reads, use decontam to identify and remove contaminant ASVs (Davis et al., 2018; Salter et al., 2014).
Confirm that the positive control (mock community) recovers the expected taxa at the expected relative abundances.
Document all QIIME 2 and DADA2 parameters in the project README and in the analysis scripts. Reproducibility requires recording the exact software versions, classifier version, and truncation lengths.
Archive raw FASTQ files, the QIIME 2 artifacts, and the analysis scripts in the project GitHub repository per SOP-L801.

8 References

Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., et al. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 37(8), 852–857. https://doi.org/10.1038/s41587-019-0209-9

Callahan, B. J., McMurdie, P. J., & Holmes, S. P. (2017). Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. The ISME Journal, 11(12), 2639–2643. https://doi.org/10.1038/ismej.2017.119

Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581–583. https://doi.org/10.1038/nmeth.3869

Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A., & Callahan, B. J. (2018). Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome, 6(1), 226. https://doi.org/10.1186/s40168-018-0605-2

McMurdie, P. J., & Holmes, S. (2013). Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLOS ONE, 8(4), e61217. https://doi.org/10.1371/journal.pone.0061217

Rideout, J. R., He, Y., Navas-Molina, J. A., Walters, W. A., Ursell, L. K., Gibbons, S. M., Chase, J., McDonald, D., Gonzalez, A., Robbins-Pianka, A., et al. (2014). Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences. PeerJ, 2, e545. https://doi.org/10.7717/peerj.545

Salter, S. J., Cox, M. J., Sherrill-Mix, S., Turner, R. M., Sherrill-Mix, S., Sherrill-Mix, S., & Walker, A. W. (2014). Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biology, 12, 87. https://doi.org/10.1186/s12915-014-0087-z

9 Related SOPs

SOP	Title	Relationship
L700	DNA Extraction	Upstream: sample preparation
L703	PCR and Gel Electrophoresis	Amplicon verification
L704	Amplicon Library Prep	Upstream: library preparation
L706	eDNA Metabarcoding	eDNA-specific application
L801	RStudio and GitHub	Data management and version control
L802	Quality Control and QA	General QA/QC framework

10 Training Record

Before running the bioinformatics pipeline independently, personnel must:

Read this SOP in full
Complete the QIIME 2 “Moving Pictures” tutorial (https://docs.qiime2.org/2024.2/tutorials/moving-pictures/)
Process a test dataset under supervision, from import through taxonomy assignment and export to R
Demonstrate correct interpretation of DADA2 denoising statistics and quality plots
Be signed off by the PI or lab manager