biogridpy

biogridpy is a small Python client library for the BioGRID REST web service, written by Kevin Arvai (GitHub user arvkevi) and released under the MIT license. BioGRID is a curated repository of genetic and protein-protein interactions (see BioGRID), and its REST endpoint exposes those interactions programmatically over HTTPS. biogridpy wraps that endpoint so that Python code can query interaction records without hand-assembling HTTP requests, making it a thin convenience layer between an application and the upstream service rather than a standalone database or file-conversion engine.

Access requires a free BioGRID API key, which the user places in a configuration file (biogridpyrc); the client is then instantiated with the path to that file and installed by cloning the repository and running setup.py, or via Bioconda (conda install biogridpy, or the corresponding biocontainers image). The primary method targets the interactions endpoint: the first positional argument selects a result format and remaining keyword arguments mirror BioGRID's documented parameter list, so queries can be filtered by gene names, evidence and interaction types, and organism taxonomy IDs. Supported result formats are BioGRID's own tabular and JSON representations - tab1, tab2, extendedTab2 (the PSICQUIC-style MITAB tabular family, related to PSI-MI Tab), plus json and jsonExtended. Results can be written to a file or loaded into a pandas DataFrame for downstream analysis.

In the graph-data ecosystem, biogridpy sits at the data-acquisition stage rather than the graph-modeling or interchange stage. It retrieves an interaction table; turning that table into a network object is left to the caller, who would typically feed the DataFrame or the tabular output into a graph library such as NetworkX or igraph, or into an interaction-standards toolkit like PSICQUIC or JAMI / PSI-JAMI that natively understands MITAB and PSI-MI XML. This division of labor is deliberate: the library's scope is querying, not format translation or visualization.

The project is minimal and lightly maintained - a handful of commits, no tagged releases beyond the 0.1.1 Bioconda build, and documentation that is largely example notebooks. Its only hard dependency is Python (pandas being needed in practice for the DataFrame feature). For simple, scripted retrieval of BioGRID interactions it is serviceable, but users needing active maintenance, richer parsing, or direct network construction should treat it as a starting point and combine it with the general-purpose graph and interaction tools above.