string-database

Query the STRING database API for protein-protein interaction networks and enrichment analysis.

8|Updated Jan 13, 2026

Search Tags:#bioinformatics #pathway analysis #systems biology #string database #protein interactions #gene ontology

Ready to run

npx skills add https://github.com/hxk622/TokenDance --skill string-database-hxk622

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: string-database
Source: https://github.com/hxk622/TokenDance/tree/main/backend/app/skills/builtin/scientific/bioinformatics/string-database
Command: npx skills add https://github.com/hxk622/TokenDance --skill string-database-hxk622

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides access to the STRING database, enabling users to query vast amounts of protein-protein interaction data and perform functional enrichment analysis, crucial for understanding complex biological systems.

Core Features & Use Cases

Protein-Protein Interaction (PPI) Networks: Retrieve and visualize interaction networks for single or multiple proteins.
Functional Enrichment: Analyze lists of proteins to identify enriched Gene Ontology (GO) terms, KEGG pathways, and other functional annotations.
Use Case: A researcher has a list of differentially expressed genes from an experiment and wants to understand the biological pathways they are involved in. This Skill can take the gene list, find their interaction partners, and identify significantly enriched GO terms and KEGG pathways.

Quick Start

Use the string-database skill to find the protein-protein interaction network for the human protein TP53 with high confidence.

Frequently Asked Questions about string-database

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

FAQPage Schema

How do I find protein-protein interaction networks for a list of differentially expressed genes?▼

To find protein-protein interaction networks for a list of differentially expressed genes, you can query the STRING database API to retrieve interaction partners and map identifiers across multiple species. This Skill handles network visualization and functional enrichment analysis for the provided gene list.

What is functional enrichment analysis for Gene Ontology and KEGG pathways?▼

Functional enrichment analysis for Gene Ontology and KEGG pathways is a statistical testing method used to identify significantly over-represented biological annotations and functional modules within a given list of proteins. This Skill automates that analysis using Python scripts.

Can I retrieve high confidence interaction partners for a specific protein like human TP53?▼

Yes, you can retrieve high confidence interaction partners for a specific protein like human TP53 by querying the STRING database. The Skill supports single or multiple protein inputs to construct and visualize targeted interaction networks.

Does this method support identifier mapping across multiple species?▼

Yes, identifier mapping is supported across multiple species. The Skill queries the STRING database API to map your provided identifiers and retrieve species-specific protein-protein interaction networks and functional annotations.

What is the best way to analyze biological pathways from a list of proteins?▼

The best way to analyze biological pathways from a list of proteins is to perform functional enrichment analysis using the STRING database. This Skill identifies significantly enriched Gene Ontology terms and KEGG pathways to help you understand complex biological systems.