What organisms are supported by the STRING database skill?

The skill supports over 5,000 organisms, including common model organisms like Human (NCBI taxon 9606), Mouse (10090), and Fruit Fly (7227).

What types of functional enrichment analysis can I perform?

You can perform enrichment analysis for Gene Ontology (GO) Biological Process, Molecular Function, and Cellular Component, as well as KEGG pathways, Pfam domains, and UniProt keywords.

Can I filter for high-confidence interactions only?

Yes, you can adjust the confidence score threshold from 150 (low) to 900 (highest), with 400 being the standard medium-confidence default.

Does the skill provide network visualizations?

Yes, it can generate PNG images of interaction networks with various 'flavors' such as evidence-based coloring, confidence-based line thickness, or molecular action types.

How does this skill handle gene name mapping?

The skill includes an ID mapping function that converts common gene symbols, protein names, and external database IDs into canonical STRING identifiers for accurate querying.

STRING Database Analysis

Name: STRING Database Analysis
Author: lifangda

bylifangda

•

Data Science & ML

Queries the STRING database to analyze protein-protein interaction networks and perform comprehensive functional enrichment for systems biology.

About

This skill empowers researchers and bioinformaticians to interact directly with the STRING database, covering over 59 million proteins and 20 billion interactions across 5,000+ organisms. It provides a robust interface for mapping gene identifiers, retrieving high-confidence interaction networks, and performing statistical enrichment analysis for GO terms, KEGG pathways, and protein domains. By integrating these capabilities into Claude, users can automate complex systems biology workflows, identify hub proteins, and generate evidence-based network visualizations for scientific research.

Key Features

Generating network visualizations with evidence-based or action-based coloring
Retrieving interaction networks with customizable confidence thresholds
Calculating PPI enrichment p-values to validate functional modules
Mapping diverse gene and protein identifiers to canonical STRING IDs
Performing functional enrichment analysis (GO, KEGG, Pfam, InterPro)
16 GitHub stars

Use Cases

Interpreting differential expression results by identifying enriched biological pathways
Expanding a list of seed proteins to discover novel interaction partners and hub proteins
Conducting cross-species comparisons of protein networks and homology relationships

About

Key Features

Generating network visualizations with evidence-based or action-based coloring
Retrieving interaction networks with customizable confidence thresholds
Calculating PPI enrichment p-values to validate functional modules
Mapping diverse gene and protein identifiers to canonical STRING IDs
Performing functional enrichment analysis (GO, KEGG, Pfam, InterPro)
16 GitHub stars

Use Cases

Interpreting differential expression results by identifying enriched biological pathways
Expanding a list of seed proteins to discover novel interaction partners and hub proteins
Conducting cross-species comparisons of protein networks and homology relationships