# Bioconductor Stringdb

Tags: [bioconductor, r, proteomics, vignette-grounded]

- Repository: https://github.com/bioMate-AI/biomate-bioconductor-kb (793 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-06-20
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-stringdb -g -a claude-code -y`
Codex: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-stringdb -g -a codex -y`
Cursor: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-stringdb -g -a cursor -y`
Gemini CLI: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-stringdb -g -a gemini-cli -y`

## Description

tags: [bioconductor, r, proteomics, vignette-grounded]

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# STRINGdb

## Dependencies & Environment

> Package-intrinsic requirements from the Bioconductor landing page, reproduce in any R environment.

- **Version:** 2.24.0 · **Bioconductor:** 3.23 · **R:** ≥ 4.6
- **Imports:** png, sqldf, plyr, igraph, httr, RColorBrewer, gplots, hash, plotrix
- **System requirements:** URL
- **Install:** `BiocManager::install("STRINGdb")`

## When to Use
- Mapping a list of differentially expressed genes to STRING database identifiers using the `map` method.
- Computing functional enrichment (e.g., Gene Ontology, KEGG) for a set of proteins using `get_enrichment`.
- Visualizing protein-protein interaction networks with custom node colors based on log fold-change using `add_diff_exp_color` and `plot_network`.
- Retrieving specific interaction partners and experimental evidence scores for proteins of interest using `get_interaction_partners`.
- Extracting network clusters/communities from a mapped dataset using `get_clusters`.

## When NOT to Use
- For offline network analysis with custom, non-STRING interaction networks, use `igraph` instead because STRINGdb is designed to query the online STRING database.
- For primary differential expression analysis of microarray data, use `limma` instead because STRINGdb requires pre-analyzed differential expression results (e.g., p-values and log fold changes).

## Data Requirements
- A data frame containing gene identifiers (e.g., HUGO names, Entrez GeneID, ENSEMBL proteins) and optional numeric columns like `pvalue` or `logFC`.
- NCBI taxonomy identifiers for the organism of interest (e.g., 9606 for Human, 10090 for Mouse).

The rest: https://github.com/bioMate-AI/biomate-bioconductor-kb/blob/HEAD/skills/proteomics/stringdb/SKILL.md