Facilitating bootstrapped and rarefaction-based microbiome diversity analysis with q2-boots

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Microbiome diversity analysis is crucial for understanding microbial communities.
• Traditional methods may not fully capture the variability within microbiome data.
• QIIME 2 is a widely used platform for microbiome data analysis.

Purpose of the Study:

• To introduce q2-boots, a QIIME 2 plugin for bootstrapped and rarefaction-based microbiome diversity analysis.
• To provide users with tools to apply various alpha and beta diversity metrics to resampled feature tables.
• To enhance the reliability and comprehensiveness of microbiome diversity assessments.

Main Methods:

• The q2-boots plugin offers eight new actions within QIIME 2.
• It supports thirty alpha diversity metrics and twenty-two beta diversity metrics.
• Users can apply these metrics to bootstrapped or rarefied feature tables via pipeline or action commands.

Main Results:

• The `qiime boots core-metrics` command resamples feature tables multiple times (n iterations).
• Alpha and beta diversity metrics are computed on each resampled table.
• Results are integrated into summary data artifacts compatible with downstream QIIME 2 tools.

Conclusions:

• Integrating distance matrices from resampled tables presented a challenge, addressed by q2-boots's three novel options.
• These integration approaches yielded highly correlated results, indicating robustness.
• q2-boots is a free, open-source tool with available source code and documentation for ease of use and implementation.