Can I visualize protein networks directly with this skill?

Yes, the skill can generate network visualizations as PNG images with different flavors, including evidence-based coloring, confidence-based line thickness, and action-based relationships.

What organisms are supported by the STRING database skill?

The skill supports over 5,000 species, including common model organisms like humans (9606), mice (10090), fruit flies (7227), and yeast (4932).

How does the skill handle different protein naming conventions?

It includes a mapping operation that converts gene names, protein names, and various external IDs into canonical STRING identifiers to ensure query accuracy.

What types of functional enrichment does it perform?

It performs enrichment analysis across multiple categories including Gene Ontology (GO), KEGG pathways, Pfam domains, InterPro families, and UniProt keywords.

STRING Protein Interaction Database

Name: STRING Protein Interaction Database
Author: K-Dense-AI

byK-Dense-AI

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2,188

Ciencia de Datos y ML

Analyzes protein-protein interaction networks and performs functional enrichment using the STRING database's 20 billion interactions.

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This skill connects Claude to the STRING database, providing a robust interface for systems biology and bioinformatics research. It allows users to query protein-protein interaction (PPI) networks for over 5,000 species, perform functional enrichment analysis across GO and KEGG pathways, and generate visual network representations. By automating ID mapping and statistical testing for network connectivity, it streamlines the interpretation of proteomics and genomics data directly within the development environment.

Características Principales

Functional enrichment analysis for GO terms, KEGG, and Pfam domains
Identifier mapping for genes and proteins across 5,000+ species
Statistical PPI enrichment testing to validate functional modules
Retrieval of interaction networks with customizable confidence thresholds
2,188 GitHub stars
Automated network visualization with evidence-based coloring

Casos de Uso

Interpreting proteomics data by identifying significant functional pathways
Building and visualizing protein interaction networks for research publications
Discovering novel protein partners and analyzing network connectivity patterns

Acerca de

Características Principales

Functional enrichment analysis for GO terms, KEGG, and Pfam domains
Identifier mapping for genes and proteins across 5,000+ species
Statistical PPI enrichment testing to validate functional modules
Retrieval of interaction networks with customizable confidence thresholds
2,188 GitHub stars
Automated network visualization with evidence-based coloring

Casos de Uso

Interpreting proteomics data by identifying significant functional pathways
Building and visualizing protein interaction networks for research publications
Discovering novel protein partners and analyzing network connectivity patterns