pyBioDataFuse

💪 Getting Started

We introduce BioDataFuse, a query-based Python tool for seamless integration of biomedical databases. BioDataFuse establishes a modular framework for efficient data wrangling, enabling context-specific knowledge graph creation and supporting graph-based analyses. With a user-friendly interface, it enables users to dynamically create knowledge graphs from their input data. Supported by a robust Python package, pyBiodatafuse, this tool excels in data harmonization, aggregating diverse sources through modular queries. Moreover, BioDataFuse provides plugin capabilities for Cytoscape and Neo4j, allowing local graph hosting. Ongoing refinements enhance the graph utility through tasks like link prediction, making BioDataFuse a versatile solution for efficient and effective biological data integration.

To know more about the package, read our documentation here.

Creating your own graph

To generate your own graph, check out our tutorial notebook in examples.

We support exporting of the graphs in Cytoscape, Neo4J and GraphDB. You can use the following functions:

# on neo4j
neo4j.load_graph(pygraph, uri="bolt://localhost:7687", username="YOUR_USERNAME", password="YOUR_PASSWORD")  # change username and password

# on cytoscape
cytoscape.load_graph(pygraph, network_name="YOUR_CUSTOM_NAME")

# rdf ttl files
bdf = BDFGraph(
    base_uri="https://biodatafuse.org/YOUR_CUSTOM_NAME/",
    version_iri="https://biodatafuse.org/example/YOUR_CUSTOM_NAME.ttl",
    orcid="YOUR_ORCID",
    author="YOUR_NAME",
)

bdf.generate_rdf(combined_df, combined_metadata)  # Generate the RDF from the (meta)data files from the example runs
bdf.serialize(
    "YOUR_CUSTOM_NAME.ttl",
    format="ttl",
)

🚀 Installation

The most recent release can be installed from PyPI with:

$ pip install pyBiodatafuse

The most recent code and data can be installed directly from GitHub with:

$ pip install git+https://github.com/BioDataFuse/pyBiodatafuse.git

👐 Contributing

Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See CONTRIBUTING.md for more information on getting involved.

👋 Attribution

⚖️ License

The code in this package is licensed under the MIT License.

📖 Citation

The work was started as part of the Elixir BioHackathon 2023 integrating and bringing together multiple Core Data Resources together.

Gadiya, Y., Ammar, A., Willighagen, E., Martinat, D., Sima, A. C., Balci, H., & Abbassi Daloii, T. (2023). BioHackEU23 report: Extending interoperability of experimental data using modular queries across biomedical resources. BioHackrXiv Preprints. https://doi.org/10.37044/osf.io/mhsqp

🍪 Cookiecutter

This package was created with @audreyfeldroy's cookiecutter package using @cthoyt's cookiecutter-snekpack template.

🛠️ For Developers

$ git clone git+https://github.com/BioDataFuse/pyBiodatafuse.git
$ cd pyBiodatafuse
$ pip install -e .

$ tox

$ git clone git+https://github.com/BioDataFuse/pyBiodatafuse.git
$ cd pyBiodatafuse
$ tox -e docs
$ open docs/build/html/index.html

$ tox -e finish