PathwaySeeker

PathwaySeeker integrates proteomics and metabolomics data, map reactions, recover balanced equations, and visualize metabolic networks as interactive graphs.
Designed to accelerate multi-omics exploration in environmental, industrial, or discovery-driven contexts.

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Installation

1. Create a virtual environment with Conda

conda create -n pathseeker python=3.11
conda activate pathseeker

2. Install dependencies

pip install -r requirements.txt

3. (Optional) Register the kernel in Jupyter

python -m ipykernel install --user --name=pathseeker --display-name "Python (pathseeker)"

Pipeline Execution

The pipeline is modular: each step can be run in the terminal (script mode) or in Jupyter notebooks (exploratory mode).

📌 Multiomics graph - Jupyter Notebook

To run in Jupyter notebook:

jupyter lab

If you have Proteomics and Metabolomics data to work: Open notebooks/multiomics_graph_proteomics&metabolomics.ipynb and run the pipeline step by step.

If you have only Proteomics (or Transcriptomics) data to work: Open notebooks/multiomics_graph_only_proteomics.ipynb and run the pipeline step by step.

Outputs

• matched_metabolites_reactions_all.csv → final table with reactions and equations
• graph_all.html → interactive graph, viewable in any browser
• graph_all.json → JSON representation of the graph (nodes, edges, attributes), compatible with PathwayViz

Important Notes

Between Step 4 and Step 5, manual curation of the data is recommended before proceeding.

The graph can be visualized either in a web browser or embedded in Jupyter.

Visualization module

AI

Termodynamic analysis

Authors

• Lummy M O Monteiro - multiomics graph
• Marjolein T Oostrom - pathway_viz
• Niaz Chowdhury - MDF
• Sutanay Choudhury - AI module