The Bacteria Analysis module performs taxonomic profiling of bacterial communities from sequencing data, enabling detailed exploration of microbial composition and diversity.
What This Module Does
This module is designed to answer a fundamental question:
Which bacteria are present in my samples, and in what relative abundance?
It provides a standardized, reproducible workflow for profiling bacterial communities across samples, cohorts, or experimental conditions.
Common use cases include:
- Microbiome research
- Environmental sampling
- Host-associated microbial studies
- Comparative cohort analysis
Input Data
Required Inputs
- FASTQ files
- Paired-end or single-end sequencing reads
- Supports common Illumina formats
Optional Inputs
- Metadata table (CSV / TSV)
- Sample identifiers
- Cohort or condition labels
- Experimental variables
Including rich metadata enables stratified analysis, cohort comparisons, and more informative visualizations.
Processing & Algorithms
The Bacteria Analysis pipeline combines well-established tools with additional normalization and filtering layers.
🔬 Core Algorithms
Kraken2 — fast, k-mer–based taxonomic classification
Bracken — accurate abundance estimation from Kraken2 results
Additional Processing
- Custom normalization layers
- Low-abundance filtering
- Sample-level quality checks
All steps are containerized and versioned to ensure full reproducibility.
Algorithm versions, reference databases, and parameters are recorded for every run, enabling exact re-execution at any time.
Output & Results
After the pipeline completes, results are available through interactive dashboards and exportable artifacts.
Visual Outputs
- Relative abundance bar plots
- Heatmaps across samples or cohorts
- Taxonomic composition summaries
Tabular Outputs
- Taxonomic abundance tables
- Normalized count matrices
- Diversity summaries
Diversity Metrics
- Shannon index
- Simpson index
- Observed taxa (if enabled)
Use interactive heatmaps and abundance plots to quickly identify dominant taxa, outliers, or cohort-specific signatures.
Best Practices
- Use consistent sequencing depth across samples
- Apply the same reference database when comparing cohorts
- Validate metadata alignment with sample IDs
- Interpret low-abundance taxa cautiously
Taxonomic assignments depend on reference databases and read quality. Results should always be interpreted in biological and experimental context.
What’s Next?
- Functional profiling modules
- Diversity and differential abundance analysis
- Integration with host genomics and AI models