Medical-research-skills string-database

Source: https://github.com/aipoch/medical-research-skills

When to Use

• You have gene symbols (e.g.,

  TP53

  ) and need to resolve them to STRING protein identifiers for downstream analysis.
• You want to retrieve a protein–protein interaction (PPI) network (functional/physical) with confidence scores for one or more proteins.
• You need to find interaction partners for a target protein to expand a candidate list (e.g., add top N neighbors).
• You want to perform functional enrichment (GO/KEGG/Reactome, etc.) for a protein set to interpret biological themes.
• You need a quick static visualization (PNG/SVG) of a STRING network for reports or notebooks.

Key Features

• ID Mapping: Convert gene/protein names to STRING identifiers for a given organism.
• Network Retrieval: Fetch interaction edges with confidence scores from STRING.
• Interaction Partners: Expand a protein list by retrieving interaction partners.
• Enrichment Analysis:
  • Functional enrichment (e.g., GO, KEGG, Reactome)
  • PPI enrichment statistics
  • Functional annotations (e.g., PFAM/SMART where supported by STRING endpoints)
• Visualization: Download static network images (PNG/SVG).

Dependencies

• Python

  >=3.8

• requests

  (tested with

  >=2.28

  )

• pandas

  (tested with

  >=1.5

  )

Install:

pip install requests pandas

Example Usage

from scripts.string_api import StringClient

def main():
    # STRING does not require a secret API key, but providing a caller identity is recommended.
    client = StringClient(caller_identity="my_analysis_tool")

    # 1) Map an identifier (e.g., TP53 in Homo sapiens; NCBI taxonomy ID 9606)
    protein_id = client.map_id(identifier="TP53", species=9606)
    print("Mapped ID:", protein_id)

    # 2) Download a network image and expand by adding interaction partners
    client.get_network_image(
        identifiers=[protein_id],
        output_file="tp53_network.png",
        add_color_nodes=10,  # add 10 partners
    )
    print("Saved network image to tp53_network.png")

    # 3) Run PPI enrichment for the set
    ppi_stats = client.get_ppi_enrichment(identifiers=[protein_id])
    print("PPI enrichment:", ppi_stats)

if __name__ == "__main__":
    main()

Implementation Details

• Client entry point:

  scripts/string_api.py

  provides the main wrapper (e.g.,

  StringClient

  ) around the STRING REST API.
• Caller identity:
  • STRING endpoints do not require an API key.
  • A

    caller_identity

    string is strongly recommended (project name/email/URL) to support rate/load management.
  • Pass it at initialization (e.g.,

    StringClient(caller_identity="my_email@example.com")

    ) or inject via environment variables in your own wrapper.
• Organism selection:
  • Most operations require a species identifier (commonly NCBI taxonomy ID, e.g.,

    9606

    for human).
• Network retrieval and scoring:
  • Network endpoints return interactions with confidence scores; downstream filtering is typically done by applying a score threshold in your analysis code (if exposed by the wrapper).
• Visualization:
  • Static images are retrieved directly from STRING image endpoints and written to disk (PNG/SVG depending on the method/parameters).
• Reference documentation:
  • See

    references/string_reference.md

    for original API notes and endpoint details included with this skill.