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Tourmaline: A containerized workflow for rapid and iterable amplicon sequence analysis using QIIME 2 and Snakemake
Luke R Thompson1,2, Sean R Anderson1,2, Paul A Den Uyl3
1Northern Gulf Institute, Mississippi State University, Mississippi State, MS 39762, USA.
Gigascience
|July 28, 2022
Summary
Tourmaline automates environmental DNA (eDNA) metabarcoding analysis using QIIME 2, streamlining diversity surveys. This Python workflow enhances data reproducibility and accessibility for ecological research.
Area of Science:
- Environmental genomics
- Bioinformatics
- Ecology
Background:
- Amplicon sequencing (metabarcoding) is crucial for surveying environmental community diversity.
- QIIME 2 is a popular, functional software for amplicon data analysis.
- Current QIIME 2 analyses lack automation, hindering efficiency and data sharing.
Purpose of the Study:
- To develop an automated workflow for QIIME 2-based amplicon data analysis.
- To improve efficiency, standardization, and reproducibility in metabarcoding studies.
- To facilitate meta-analysis and sharing of environmental diversity data.
Main Methods:
- Developed Tourmaline, a Python-based workflow using Snakemake and QIIME 2.
- Implemented DADA2 or Deblur for denoising, followed by taxonomic assignment.
- Included diversity analyses (alpha, beta) and automated HTML report generation.
- Incorporated features like multi-core support, quality-based trimming, and sequence filtering.
Main Results:
- Tourmaline successfully processed a 16S ribosomal RNA amplicon dataset.
- Demonstrated utility for parameter optimization and interactive visualization.
- Generated comprehensive HTML reports linking to QIIME 2 viewer and notebooks.
Conclusions:
- Automated workflows like Tourmaline accelerate amplicon data analysis.
- Tourmaline reduces the time from data generation to actionable ecological insights.
- The workflow enhances reproducibility and accessibility of metabarcoding results.
