Does it require specific protein IDs?

While it works best with STRING IDs, the skill includes a 'string_map_ids' function to automatically convert gene names, protein names, and external IDs into the correct canonical identifiers.

Can I use this for non-human species?

Yes, the skill supports over 5,000 organisms, including common model organisms like mouse (Mus musculus), yeast (Saccharomyces cerevisiae), and fruit fly (Drosophila melanogaster).

What is the STRING Database skill?

It is a specialized capability for Claude that provides programmatic access to the STRING database, allowing users to query protein networks and biological pathways directly within their development environment.

What types of functional enrichment are supported?

The skill performs enrichment analysis for Gene Ontology (Biological Process, Molecular Function, Cellular Component), KEGG Pathways, Pfam domains, InterPro, SMART, and UniProt Keywords.

STRING Database Analysis

Name: STRING Database Analysis
Author: K-Dense-AI

byK-Dense-AI

•

3,719

Data Science & ML

Accesses and analyzes protein-protein interaction networks and functional enrichment data using the STRING API.

About

This skill integrates the STRING database into the Claude environment, enabling researchers and bioinformaticians to query over 20 billion protein interactions across 5,000+ species. It simplifies complex systems biology tasks such as mapping protein identifiers, retrieving interaction networks, performing GO/KEGG enrichment analysis, and generating network visualizations. By providing structured access to evidence-based biological data, it streamlines the process of discovering interaction partners and validating functional modules within protein lists for proteomics and genomics research.

Key Features

Generate visual interaction network diagrams with evidence or confidence coloring
Perform functional enrichment analysis across GO, KEGG, Pfam, and InterPro
Test network connectivity significance with PPI enrichment analysis
Retrieve protein-protein interaction (PPI) networks with evidence-based scoring
Map gene and protein identifiers across 5,000+ species via REST API
3,719 GitHub stars

Use Cases

Validating if a set of proteins forms a statistically significant biological module
Analyzing differential expression or proteomics results to identify functional pathways
Identifying hub proteins and potential novel interaction partners for target genes

About

Key Features

Generate visual interaction network diagrams with evidence or confidence coloring
Perform functional enrichment analysis across GO, KEGG, Pfam, and InterPro
Test network connectivity significance with PPI enrichment analysis
Retrieve protein-protein interaction (PPI) networks with evidence-based scoring
Map gene and protein identifiers across 5,000+ species via REST API
3,719 GitHub stars

Use Cases

Validating if a set of proteins forms a statistically significant biological module
Analyzing differential expression or proteomics results to identify functional pathways
Identifying hub proteins and potential novel interaction partners for target genes