ppi-network-for-gene-list

To build a protein-protein interaction network from a gene list, provide comma-separated gene symbols and a species identifier. The script fetches interactions via the STRING API and generates a connected network with edge scores.

Network module detection and hub gene identification are used to prioritize candidate genes from GWAS or screens by evaluating network centrality. This helps explore functional modules and pathway context within a gene set.

Yes, you can use Python to calculate network metrics like density and connected components for PPI networks. This skill uses pandas and numpy to compute per-gene statistics and generate a comprehensive network summary.

No, this PPI network analysis tool does not require manual interaction with the STRING database. It automates fetching interactions directly through the STRING API, handling species selection and interaction score cutoffs internally.

The best way to generate publication-ready visualizations for protein-protein interaction networks is using matplotlib with deterministic layouts. This approach produces high-quality network plots alongside structured edge lists and node metrics.

Using a fixed random seed for network layout and betweenness approximation ensures reproducible results across runs. The limitation is deterministic outputs, meaning visual layouts and metric approximations will not vary between executions.