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LeonChaoX
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string-database

Retrieve STRING protein-protein interaction networks and functional enrichment results from protein identifiers.

783|65|Updated Feb 27, 2026
Search Tags:#network visualization#api integration#systems biology#string#functional enrichment#protein-protein interactions#go kegg
One-click install
npx skills add https://github.com/LeonChaoX/qinyan-academic-skills --skill string-database-leonchaox
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: string-database
Source: https://github.com/LeonChaoX/qinyan-academic-skills/tree/main/skills/12-%E7%A7%91%E5%AD%A6%E6%95%B0%E6%8D%AE%E5%BA%93/string-database
Command: npx skills add https://github.com/LeonChaoX/qinyan-academic-skills --skill string-database-leonchaox

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill turns a list of proteins into actionable STRING protein-protein interaction (PPI) networks and interpretable functional results, removing the manual effort of building networks and running pathway/GO enrichment by hand.

Core Features & Use Cases

  • Protein identifier mapping: Convert gene names and external identifiers into STRING IDs for the correct species context.
  • PPI network retrieval: Pull interaction edges with confidence scoring and evidence channels, including functional vs physical interaction modes.
  • Functional enrichment: Run GO/KEGG/Pfam-style enrichment on protein sets to interpret biological meaning.
  • Network validation & visualization: Test whether a protein set is significantly connected (PPI enrichment) and generate evidence/confidence/action-flavored network PNG figures.
  • Interaction expansion: Discover top interaction partners to grow a network from seed proteins.
  • Homology and version reproducibility: Retrieve homology information and obtain STRING database version for traceability.

Quick Start

Use the string-database skill with the protein names and a species taxon ID to retrieve STRING interaction networks, run enrichment, and produce an annotated network image.

Frequently Asked Questions about string-database

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I retrieve a protein-protein interaction network from STRING using a list of gene names?▼

You can retrieve a STRING protein-protein interaction network by mapping gene names to STRING IDs, specifying the species taxon, and using the provided Python helper functions to pull interaction edges via REST API calls.

How does functional enrichment work with STRING protein interaction networks?▼

Functional enrichment applies GO, KEGG, and Pfam-style analysis to a set of STRING protein identifiers to identify significantly overrepresented biological pathways and annotations within the protein-protein interaction network.

Can I validate network connectivity for my protein set using STRING?▼

Yes, you can run a PPI enrichment test to validate network connectivity and determine whether your protein set exhibits significantly more protein-protein interactions than expected for a random set of proteins in the STRING database.

Do I need to specify a species taxon ID to map protein identifiers for STRING database queries?▼

Yes, specifying a species taxon ID is required for correct STRING identifier mapping, ensuring that gene names and external identifiers are accurately converted to STRING IDs within the correct biological context for network retrieval.

How do I expand interaction partners from seed proteins in a STRING PPI network?▼

You can expand interaction partners by querying the STRING database with your seed protein identifiers to discover top interaction partners and grow your protein-protein interaction network using configurable confidence thresholds from 0 to 1000.

What confidence threshold should I use for STRING PPI network retrieval?▼

STRING PPI network retrieval uses configurable confidence thresholds ranging from 0 to 1000, allowing you to filter protein-protein interactions by evidence channels and select functional versus physical interaction modes based on your network requirements.

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