Does this skill perform statistical enrichment analysis?

Yes, it can perform functional enrichment analysis across Gene Ontology, KEGG, and Pfam databases, as well as PPI enrichment testing to see if a set of proteins has more interactions than expected by chance.

Can I visualize protein networks with this skill?

Yes, the skill can generate PNG visualizations of protein networks using different flavors, such as evidence-colored lines, confidence-based thickness, or action-based relationships like activation and inhibition.

Which species are supported for protein mapping?

The skill supports over 5,000 organisms. Common examples include Human (NCBI taxon ID 9606), Mouse (10090), and Fruit Fly (7227).

What is the STRING database?

STRING is a comprehensive global resource for known and predicted protein-protein interactions, covering over 59 million proteins and 20 billion interactions across more than 5,000 species.

STRING Protein Database Explorer

Name: STRING Protein Database Explorer
Author: henriquescastilho

byhenriquescastilho

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Data Science & ML

Queries the STRING database to analyze protein-protein interaction networks and perform functional enrichment across 5,000+ species.

The STRING Protein Database skill provides a robust interface for systems biology and proteomics research directly within Claude. It enables users to map gene identifiers, retrieve interaction networks with evidence-based scoring, and perform statistical enrichment analysis across Gene Ontology and KEGG pathways. Whether you are identifying hub proteins in a list of differentially expressed genes or visualizing protein complexes with evidence-based coloring, this skill streamlines the transition from raw genomic data to biological insight using data from over 59 million proteins.

Key Features

01Statistical PPI enrichment testing to validate if protein lists form functional modules

02Retrieval of interaction networks with customizable confidence thresholds and evidence types

031 GitHub stars

04Mapping of gene and protein names to canonical STRING identifiers across 5,000+ organisms

05Generation of high-quality network visualizations with evidence or confidence-based coloring

06Automated functional enrichment analysis for GO terms, KEGG pathways, Pfam, and InterPro

Use Cases

01Generating publication-ready interaction network visualizations with statistical validation

02Analyzing differentially expressed protein lists from proteomics or transcriptomics experiments

03Identifying novel interaction partners and hub proteins within specific biological pathways

Key Features

01Statistical PPI enrichment testing to validate if protein lists form functional modules

02Retrieval of interaction networks with customizable confidence thresholds and evidence types

031 GitHub stars

04Mapping of gene and protein names to canonical STRING identifiers across 5,000+ organisms

05Generation of high-quality network visualizations with evidence or confidence-based coloring

06Automated functional enrichment analysis for GO terms, KEGG pathways, Pfam, and InterPro

Use Cases

01Generating publication-ready interaction network visualizations with statistical validation

02Analyzing differentially expressed protein lists from proteomics or transcriptomics experiments

03Identifying novel interaction partners and hub proteins within specific biological pathways