pyBiodatafuse: extending interoperability of data using modular queries across biomedical resources

Integrating omics data analysis with publicly available databases is crucial for unravelling complex biological mechanisms. However, this integration process is often intricate and time-consuming due to the diversity and complexity of the data involved. Achieving consistent harmonization across data types is challenging when managing disparate formats and sources. To address these issues, we introduce pyBiodatafuse, a query-based Python tool designed to integrate biomedical databases. This tool establishes a modular framework that simplifies data wrangling, enabling the creation of context-specific knowledge graphs (KGs) while supporting graph-based analyses.

We developed a pipeline for generating context-specific knowledge graphs dynamically, allowing users to create KGs on the fly from a set of gene or metabolite identifiers. pyBiodatafuse features a user-friendly interface that streamlines this process, making it accessible even to researchers without extensive computational expertise. Additionally, the tool offers plugins for widely used platforms such as Cytoscape, Neo4j, and GraphDB, enabling local hosting of resulting property and RDF graphs. This versatility ensures that generated KGs can be efficiently utilized within diverse research workflows. To demonstrate its potential, we used pyBiodatafuse to create a graph for post-COVID syndrome using differential gene expression data, showcasing its ability to build adaptable and context-specific knowledge representations. Thus, pyBiodatafuse sets the stage for streamlined data integration, empowering researchers to focus on discovery and analysis without being hindered by data management complexities.

pyBiodatafuse is open-source, with its source code and PyPi package available at https://github.com/BioDataFuse/pyBiodatafuse and https://pypi.org/project/pyBiodatafuse/. The user interface can be accessed at https://biodatafuse.org/. Additionally, a release has been made on Zenodo at https://doi.org/10.5281/zenodo.18468942.