COBRAxy

A Python-based command-line suite for metabolic flux analysis and visualization, with Galaxy integration.

COBRAxy enables the integration of transcriptomics data with COBRA-based metabolic models, offering a comprehensive framework for studying metabolism in both health and disease. With COBRAxy, users can load and enrich metabolic models by incorporating transcriptomic data and adjusting the model's medium conditions. DOC: https://compbtbs.github.io/COBRAxy

Features

• Galaxy Tools - Web-based analysis with intuitive interface
• Import/Export of metabolic models in multiple formats (SBML, JSON, MAT, YAML)
• Reaction Activity Scores (RAS) - Compute metabolic activity from gene expression data
• Reaction Propensity Scores (RPS) - Infer metabolic preferences from metabolite abundance
• Flux computation - Compute metabolic flux distributions using different optimization or sampling algorithms
• Statistical Analysis - Perform statistically significant flux differences between groups of samples and report on an enriched metabolic map
• Built-in Models - Ready-to-use models including ENGRO2 and Recon3D

Requirements

• Python: 3.8-3.13
• OS: Linux, macOS, Windows (Linux/macOS recommended)
• Dependencies: Automatically installed via pip (COBRApy, pandas, numpy, etc.)
• Build tools: C/C++ compiler (gcc, clang, or MSVC), CMake for compiling Python extensions, pkg-config

Installation

Recommended: Using Conda

# Create a new conda environment
conda create -n cobraxy python=3.13 -y
conda activate cobraxy

# Install system dependencies via conda (optional, if not using system packages)
conda install -c conda-forge gcc cmake pkg-config swiglpk -y

# Clone and install COBRAxy
git clone https://github.com/CompBtBs/COBRAxy.git
cd COBRAxy/src
pip install .

Galaxy Integration

COBRAxy provides Galaxy tool wrappers (.xml files) for web-based analysis:

• Upload data through Galaxy interface
• Chain tools in visual workflows
• Share and reproduce analyses

For Galaxy installation and setup, refer to the official Galaxy documentation.

Tools

Tool	Purpose	Input	Output
importMetabolicModel	Import and extract model components	SBML/JSON/MAT/YAML model	Tabular model data
exportMetabolicModel	Export tabular data to model format	Tabular model data	SBML/JSON/MAT/YAML model
ras_generator	Compute reaction activity scores	Gene expression data	RAS values
rps_generator	Compute reaction propensity scores	Metabolite abundance	RPS values
marea	Statistical pathway analysis	RAS + RPS data	Enrichment + base maps
ras_to_bounds	Apply RAS constraints to model	RAS + SBML model	Constrained bounds
flux_simulation	Sample metabolic fluxes	Constrained model	Flux distributions
flux_to_map	Add fluxes to enriched maps	Flux samples + base maps	Final styled maps
marea_cluster	Cluster analysis	Expression/flux data	Sample clusters

Data Flow

Gene Expression    Metabolite Data    SBML Model
      ↓                   ↓               ↓
  RAS Generator      RPS Generator   Model Tables
      ↓                   ↓               
    RAS Values       RPS Values           
    | ↓                   ↓               
    | └─────────┬─────────┘               
    |           ↓                         
    |        MAREA                        
    |    (Enrichment +                    
    |     Base Maps)                      
    ↓                
    RAS Values  →  RAS to Bounds  ←── Model Tables
                        ↓
                  Constrained Model
                        ↓
                  Flux Simulation
                        ↓
                   Flux Samples
                        ↓
                   Flux to Map  ←── Maps (ENGRO2)
                        ↓
               Final Enriched Maps

Basic Workflow

# 1. Generate RAS from expression data
ras_generator -in expression.tsv -ra ras_output.tsv -rs ENGRO2

# 2. Generate RPS from metabolite data (optional)
rps_generator -id metabolites.tsv -rp rps_output.tsv

# 3. Create enriched pathway maps with statistical analysis
marea -using_RAS true -input_data ras_output.tsv -choice_map ENGRO2 -gs true -idop base_maps

# 4. Apply RAS constraints to model for flux simulation
ras_to_bounds -ms ENGRO2 -ir ras_output.tsv -rs true -idop bounds_output

# 5. Sample metabolic fluxes with constrained model
flux_simulation -ms ENGRO2 -in bounds_output/*.tsv -a CBS -ns 1000 -idop flux_results

# 6. Add flux data to enriched maps
flux_to_map -if flux_results/*.tsv -mp base_maps/*.svg -idop final_maps

Troubleshooting

Common issues:

• Missing GLPK: Install glpk-utils and swiglpk for optimal CBS performance
• SVG errors: Install libvips system library
• Memory issues: Reduce sampling count (-ns) or use fewer batches (-nb)

Contributing

Contributions welcome! Please:

• Follow existing code style
• Add documentation for new features
• Test with provided example data
• Submit focused pull requests

Citation

If you use COBRAxy in research, please cite:

• COBRApy for core metabolic modeling
• MaREA for enrichment methods
• This repository for integrated workflow

Links

• COBRApy Documentation
• Galaxy Project
• GSoC 2024 Project