DADA2 Workflow for 16S rRNA Sequencing Analysis

A generalized workflow for processing 16S rRNA gene amplicon data using the DADA2 package in R. This workflow identifies exact amplicon sequence variants (ASVs) with higher resolution than traditional OTU-based methods.

Overview

This repository contains an RMarkdown workflow that implements the complete DADA2 pipeline:

1. Quality filtering - Remove low-quality reads
2. Error rate learning - Learn error rates from the data
3. Dereplication - Combine identical reads
4. Sample inference - Apply the DADA2 algorithm
5. Merger of paired-end reads - Align and merge paired reads
6. Chimera removal - Remove artifactual sequences
7. Taxonomic assignment - Classify ASVs
8. Output generation - Create tables and visualizations

Requirements

• R ≥ 4.0.0
• Required R packages:
  • dada2
  • ggplot2
  • phyloseq
  • Biostrings
  • ShortRead
  • tidyverse

Getting Started

1. Clone this repository:

   git clone https://github.com/yourusername/dada2-workflow.git
   cd dada2-workflow

2. Install required R packages:

   install.packages(c("ggplot2", "tidyverse"))
   if (!requireNamespace("BiocManager", quietly = TRUE))
       install.packages("BiocManager")
   BiocManager::install(c("dada2", "phyloseq", "Biostrings", "ShortRead"))

3. Reference database:

   • The workflow uses the Silva database (v138.1) for taxonomy assignments
   • The database is either accessed from the DADA2 package or downloaded from Zenodo
   • Both genus-level and species-level assignments are performed

4. Open dada2_workflow.Rmd in RStudio and update the following:

   • Path to your sequence files (supports both _R1_001.fastq.gz and _R1.fastq.gz naming conventions)
   • Read trimming parameters based on your data quality
   • Path to your sample metadata file

5. Execute the workflow by running all code chunks in the RMarkdown document.

6. View results in the interactive dashboard:

   # Install required packages if needed
   install.packages(c("shiny", "shinydashboard", "plotly", "DT", "vegan", "viridis"))
   
   # Make sure phyloseq is installed
   if (!requireNamespace("BiocManager", quietly = TRUE))
       install.packages("BiocManager")
   BiocManager::install("phyloseq")
   
   # Run the dashboard (will open in your browser)
   source("run_dashboard.R")
   
   # If the dashboard doesn't open automatically, you can access it at:
   # http://127.0.0.1:4321
   
   # Note: If you encounter a greyed-out dashboard, try restarting R
   # and running the dashboard again

Customizing the Workflow

The main parameters to adjust in the workflow:

• Read trimming (truncLen, maxEE): Modify based on your sequencing quality
• Taxonomy thresholds: Adjust confidence thresholds for taxonomic assignments
• Visualization options: Adjust plotting parameters as needed

Output Files

The workflow produces several output files in the results/ directory:

• seqtab_nochim.csv: ASV count table
• taxonomy.csv: Taxonomic assignments for each ASV (with species-level assignments)
• phyloseq_object.rds: R object for downstream analysis
• ASVs.fasta: FASTA file containing ASV sequences

Interactive Dashboard

The included Shiny dashboard (dashboard.R) provides interactive visualization of DADA2 results:

• Overview: Quick summary statistics and sample metrics
• Sample Quality: Read count distributions and filtering statistics
• Alpha Diversity: Shannon, Simpson, Observed, and Chao1 diversity metrics
• Beta Diversity: PCoA, NMDS, t-SNE, and UMAP ordinations with PERMANOVA
• Taxonomy: Interactive bar plots and heatmaps of taxonomic composition
• ASV Table: Browse ASV sequences and abundance data
• Differential Abundance: Identify taxa that differ between sample groups using DESeq2 or ALDEx2

Launch the dashboard by running:

source("run_dashboard.R")

Contributing

Contributions to improve this workflow are welcome. Please feel free to submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

References

• Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP (2016). "DADA2: High-resolution sample inference from Illumina amplicon data." Nature Methods, 13, 581-583. doi: 10.1038/nmeth.3869
• McMurdie PJ, Holmes S (2013). "phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data." PLoS ONE, 8(4):e61217
• Quast C, et al. (2013). "The SILVA ribosomal RNA gene database project: improved data processing and web-based tools." Nucleic Acids Research, 41(D1), D590-D596. doi: 10.1093/nar/gks1219