bifidoannotator 2.0.0

Fine-grained annotation of bifidobacterial enzymes for human milk glycan utilization

bifidoAnnotator

A specialized bioinformatics pipeline for fine-grained annotation of bifidobacterial enzymes involved in human milk glycan (HMG) utilization, with publication-ready visualization capabilities.

Overview

bifidoAnnotator addresses the critical gap in annotation workflows by providing hierarchical, homology-based protein cluster-level resolution of bifidobacterial enzymes (glycoside hydrolases, GHs) associated with HMG metabolism. Unlike broad GH family-level tools, bifidoAnnotator uses a manually curated database of 22,699 reference sequences organized into 122 functional protein clusters across 13 GH families, enabling identification of specific enzyme variants rather than just general GH family assignments.

Features

• Hierarchical annotation: Multi-level GH classification with reference-specific thresholds
• Flexible input: Single file or batch processing modes
• Quality control: Configurable coverage and bit score filtering
• Comprehensive output: Detailed tables, summary matrices, and publication-ready visualizations
• Adaptive visualization: Auto-sizing heatmaps with optional manual control
• Publication quality: Vector graphics (PDF) and high-resolution raster (PNG) outputs
• Detailed logging: Complete analysis tracking

Installation (python≥3.10 required)

Conda Installation

# Install bifidoAnnotator with all dependencies and packaged database (Recommended)
conda install -c bioconda bifidoannotator

# Verify installation
bifidoAnnotator --help

pip installation (Requires separate MMseqs2 installation)

#Install MMseqs2 first
conda install mmseqs2

#Then install bifidoAnnotator
pip install bifidoannotator

#Verify installation
bifidoAnnotator --help

Manual Installation and test run

#Create conda environment
conda create -n bifidoAnnotator python=3.10
conda activate bifidoAnnotator

#Install dependencies
conda install mmseqs2
pip install pandas seaborn matplotlib scipy

#Download bifidoAnnotator and its resources
git clone https://github.com/nicholaspucci/bifidoAnnotator.git
cd bifidoAnnotator
wget https://zenodo.org/records/17206993/files/bifDB_dir.tar.gz
tar -xvzf bifDB_dir.tar.gz -C database/

#Test run on 9 bifidobacterial genomes
python script/run_bifidoAnnotator.py -d Benchmark/ --mapping_file database/mapping_file.tsv --bifdb database/bifDB_dir/bifDB -s Benchmark/genome_list_benchmark.txt  -o bifidoAnnotator_output --annotations_file Benchmark/genome_info_benchmark.txt 

Usage

Quick Start (Conda Package)

# Single genome analysis - uses packaged database automatically
bifidoAnnotator -i input_genome.faa -o output_directory

# Batch processing
bifidoAnnotator -d genome_directory -s sample_list.txt -o output_directory

# With genome metadata for enhanced visualizations
bifidoAnnotator -i genome.faa --annotations_file metadata.tsv -o results

Advanced Usage

# Custom threading and sensitivity
bifidoAnnotator -i genome.faa --threads 8 --sensitivity 7.5 -o results

# Manual figure sizing
bifidoAnnotator -i genome.faa --gh-figsize 16 20 --cluster-figsize 18 22 -o results

## Input Files

### Required Inputs

1. Protein FASTA files: Amino acid sequences in FASTA format
   - Single file: Use `-i filename.faa`
   - Multiple files: Use `-d directory/` with `-s sample_list.txt`

2. Mapping file: TSV file with reference annotations containing:
   - `Protein_Name`: Reference sequence identifiers
   - `GH_family`: GH family assignments
   - `Enzyme`: Enzyme annotations
   - `Cluster_annotation`: Cluster assignments
   - `Validation_status`: Quality indicators
   - `GH_family-F1_threshold`: Identity thresholds for GH family assignment
   - `GH-cluster threshold`: Identity thresholds for cluster assignment
   - `HMG-utilization`: HMG-utilization capacity (Yes/No/Unknown)

### Optional Inputs

4. Sample file: Text file listing genome names (required for batch mode)
   
   genome1
   genome2
   genome3

5. Annotations file: TSV file with genome metadata for enhanced heatmaps
   genome_name	species	isolation_source	other_metadata
   genome1	B. longum	infant	metadata1
   genome2	B. breve	adult	metadata2

Output Structure

├── bifidoAnnotator_log.txt
├── bifidoAnnotator_tables/
│   ├── detailed_annotations.tsv
│   ├── genome_summary.tsv
│   ├── gh_family_matrix.tsv
│   ├── enzyme_matrix.tsv
│   └── cluster_matrix.tsv
└── bifidoAnnotator_visualizations/
    ├── gh_family_heatmap.png
    ├── gh_family_heatmap.pdf
    ├── cluster_heatmap.png
    ├── cluster_heatmap.pdf
    ├── enzyme_heatmap.png
    └── enzyme_heatmap.pdf

Output Files

Tables

• detailed_annotations.tsv: Complete annotation results with sequence IDs, assignments, and quality metrics
• genome_summary.tsv: Per-genome summary with copy numbers for each GH family/cluster
• matrix files: Wide-format presence/absence matrices for downstream analysis

Visualizations

• Heatmaps: Hierarchically clustered heatmaps showing GH distribution patterns
  • Automatic adaptive sizing based on data dimensions
  • Manual size control via --gh-figsize, --cluster-figsize, --enzyme-figsize
  • Enhanced versions with genome annotations (if provided)
  • Both PNG (high-res) and PDF (vector) formats

Parameters

Core Parameters

• -i, --input_file: Single input FASTA file
• -d, --genome_directory: Directory containing multiple FASTA files
• -s, --sample_file: List of genome names for batch processing
• -o, --output_dir: Output directory (default: bifidoAnnotator_output)
• --bifdb: Path to MMseqs2 bifidobacterial database
• --mapping_file: Reference annotation mapping file

Optional Parameters

• --annotations_file: Genome metadata for enhanced visualizations
• --threads: Number of threads for MMseqs2 (default: 4)
• --sensitivity: MMseqs2 sensitivity (default: 7.5)
• -hc, --heatmap_col: Color scheme - 'red' or 'blue' (default: blue)
• --gh-figsize: GH heatmap dimensions (width height)
• --cluster-figsize: Cluster heatmap dimensions (width height)
• --enzyme-figsize: Enzyme heatmap dimensions (width height)

Annotation Thresholds

• Coverage threshold: 50% minimum alignment coverage
• Bit score threshold: 200 minimum bit score
• Identity thresholds: Reference-specific from mapping file

Example Workflows

Quality Control

The pipeline implements multiple quality control measures:

• Coverage filtering: Ensures alignments span sufficient sequence length
• Bit score thresholds: Filters low-confidence matches
• Reference-specific thresholds: Uses optimized identity cutoffs per reference sequence
• Hierarchical validation: Applies different thresholds for family vs. cluster assignment

Visualization Features

Heatmap Characteristics

• Hierarchical clustering: Automatic genome and feature clustering
• Discrete color scales: Integer copy number representation
• Publication ready: High-resolution outputs with professional styling
• Adaptive sizing: Automatic dimension calculation based on data size
• Annotation integration: Metadata bars with custom color palettes
• Blue color scheme by default (red available via --heatmap_col/-hc red)

Size Optimization

• Auto-calculates optimal dimensions based on:
  • Number of genomes (affects width)
  • Number of features (affects height)
  • Presence of annotation metadata
• Manual override available for specific requirements

Data availability

The bifDB reference database and AIMS bifidobacterial proteomes utilized to demonstrate bifidoAnnotator's capabilities are available on Zenodo (DOI: 10.5281/zenodo.17206993)

Citation

Please cite this tool in your publications. If you use this software, consider citing:

bifidoAnnotator: fine-grained annotation of bifidobacterial glycoside hydrolases for human milk glycan utilization
Authors: Nicholas Pucci & Daniel R. Mende

Links

GitHub: https://github.com/nicholaspucci/bifidoAnnotator PyPI: https://pypi.org/project/bifidoannotator/ Bioconda: (pending approval) Database (Zenodo): https://doi.org/10.5281/zenodo.17206993

Support

For questions, bug reports, or feature requests, please open an issue on GitHub or contact the authors.

License

MIT License - see LICENSE file for details

Version History

• v1.0: Complete pipeline integration with adaptive visualization
• Features hierarchical annotation, quality control, and publication-ready outputs