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Bioconductor Stringdb

Organization
bioMate-AI
bioconductor-stringdb

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

Overview

PublisherbioMate-AI
Repositorybiomate-bioconductor-kb
Skill namebioconductor-stringdb
Stars
804
Forks
66
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by bioMate-AI on GitHub. Read the source before you install it.

Installation

Install the Bioconductor Stringdb AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bioMate-AI/biomate-bioconductor-kb/tree/main/skills/proteomics/stringdb
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bioMate-AI/biomate-bioconductor-kb.git /tmp/biomate-bioconductor-kb
mkdir -p .claude/skills
cp -r /tmp/biomate-bioconductor-kb/skills/proteomics/stringdb .claude/skills/bioconductor-stringdb
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bioconductor Stringdb in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bioconductor Stringdb on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bioconductor Stringdb is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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).

Key Parameters

  • version ("12.0"): The STRING database version to query.
  • species (9606): NCBI taxonomy identifier of the organism.
  • score_threshold (400): Minimum combined confidence score for interactions to be loaded.
  • input_directory (""): Local directory path to cache downloaded STRING database files for off-line use.
  • network_type ("full"): Type of network to retrieve (e.g., "full" for functional or "physical" for physical subnetworks).
  • removeUnmappedRows (TRUE): Whether to remove rows corresponding to unmapped genes during the map step.
  • category ("Component"): The enrichment category to display when calling get_enrichment_figure.
  • target_species_id (10090): The NCBI taxonomy identifier of the target species when retrieving homologs via get_homologs_besthits.

Best Practices

  • Always specify a local input_directory when initializing the STRINGdb object to cache database files and enable off-line use.
  • Remove unassigned genomic positions (e.g., LOC genes) from your data frame before calling map to improve the mapping percentage.
  • Call set_background with your full list of mapped identifiers before running get_enrichment to ensure p-values are correctly calibrated against your specific experiment.
  • Use post_payload to upload custom color mapping (like up/down-regulation halos) to the STRING server before visualizing with plot_network.

Common Pitfalls

  • Wrong enrichment p-values: Running enrichment statistics without setting the background. Fix: Call set_background with the full set of measurable proteins in your experiment before testing.
  • Low mapping rate: Passing unassigned or unsupported identifiers (like LOC genes). Fix: Filter out non-gene probes before mapping, or set removeUnmappedRows = FALSE to manually inspect unmapped rows.
  • Network timeouts/slow execution: Re-downloading database files every session. Fix: Provide a persistent path to input_directory in the constructor to cache files locally.

Alternatives

  • igraph: For general-purpose complex network research and custom graph clustering algorithms.
  • limma: For performing the initial differential expression analysis to generate the p-values and fold changes required by STRINGdb.

Citations

  • Szklarczyk D, et al. (2021). The STRING database in 2021: association networks and functional enrichment analysis for genome-wide gene lists. Nucleic Acids Research.

References


Run this on BioMate

This skill is the knowledge layer — when, why, and how to use stringdb. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.

▶ Open stringdb on BioMate →

Frequently asked questions

What does the Bioconductor Stringdb AI skill do?

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

Why use Bioconductor Stringdb on TypingMind?

Because you install it once and use it with any model. Bioconductor Stringdb is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bioconductor Stringdb in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bioMate-AI/biomate-bioconductor-kb/tree/main/skills/proteomics/stringdb. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bioconductor Stringdb?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bioconductor Stringdb?

As many as you like. As long as a model supports skills, you can use Bioconductor Stringdb with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bioconductor Stringdb AI skill free?

It is published on GitHub by bioMate-AI. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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