Neko

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Neko is a Python package for extracting, visualizing, converting, and studying interactions from databases into executable activity flow-based models. It integrates OmniPath and other interaction resources, uses UniProt tables for identifier translation, and exports networks for tools such as Atopo.

Note: NeKo is distributed as Beta software under the PyPI name nekomata; the Python import package remains neko.

Features

  • Network creation and manipulation

  • Connection of nodes and subnetworks

  • Gene-to-phenotype mapping

  • Network visualization and export helpers

  • Interaction database integration

  • Branching network history with automatic snapshots, HTML/SVG rendering, and pruning controls

Installation

Install the nekomata distribution from PyPI. Do not confuse it with the unrelated neko or pyneko distributions.

  1. Install `NeKo` from PyPI:

    python -m pip install nekomata

Installation from Source

For the latest development version, you can still clone the repository and install directly from the source:

git clone https://github.com/sysbio-curie/Neko.git
cd Neko
python -m pip install .

This installs the latest development version from the checked-out source.

Troubleshooting

If Graphviz-related installation or rendering fails, install Graphviz using your system package manager. On Linux:

sudo apt-get install python3-dev graphviz libgraphviz-dev

On macOS:

brew install graphviz

For more details visit: https://graphviz.org/download/

Documentation

For full documentation, including API reference and detailed tutorials, visit our GitHub Pages documentation. Users upgrading an existing workflow should also read the NeKo 1.9 migration guide.

Jupyter Notebooks

We provide a comprehensive set of Jupyter notebooks that offer a detailed and user-friendly explanation of the package. These notebooks cover all modules of NeKo and provide a complete overview of how to use the package:

  1. Usage

  2. Build network using user-defined resources

  3. Stepwise connection: a focus on the INE algorithm

  4. Connect to upstream components

  5. Build network based on kinase-phosphosite interactions

  6. Connect to downstream Gene Ontology terms

  7. Map tissue expression

  8. Network comparison

  9. Re-creating famous pathways from SIGNOR and WIKIPATHWAYS using NeKo

  10. Import and complete a network

  11. Network history, branching, and visualisation

You can find these notebooks in the notebooks directory of the repository.

Features comparison with similar tools

Below you can find a table displaying the main features of NeKo compared to other similar tools: Features Table on GitHub.

Acknowledgements

This project is a collaborative effort between Institut Curie, NTNU, Saez lab and BSC.

Current contributors: Marco Ruscone, Eirini Tsirvouli, Andrea Checcoli, Dénes Turei, Aasmund Flobak, Emmanuel Barillot, Loredana Martignetti, Julio Saez-Rodriguez and Laurence Calzone.