Bioinformatics

Community

beita6969

bioinformatics

Performs bioinformatics analyses including pathway enrichment, gene ontology analysis, protein-protein interaction networks, multi-omics integration, and biological sequence database querying; trigger when users discuss gene sets, biological pathways, functional annotation, or omics data integration.

Overview

Publisher	beita6969
Repository	`ScienceClaw`
Skill name	`bioinformatics`
Stars	950
Forks	106
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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 beita6969 on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/beita6969/ScienceClaw/tree/main/skills/bioinformatics
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.

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/beita6969/ScienceClaw.git /tmp/ScienceClaw
mkdir -p .claude/skills
cp -r /tmp/ScienceClaw/skills/bioinformatics .claude/skills/bioinformatics

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bioinformatics 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 Bioinformatics 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 Bioinformatics 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.

When to Trigger

Activate this skill when the user mentions:

Pathway analysis, KEGG, Reactome, WikiPathways
Gene Ontology (GO) enrichment, biological process, molecular function
Protein-protein interaction (PPI) networks, STRING, BioGRID
Multi-omics integration (transcriptomics + proteomics + metabolomics)
Gene set enrichment analysis (GSEA), over-representation analysis (ORA)
Sequence databases, UniProt, NCBI, Ensembl queries
Single-cell RNA-seq analysis, clustering, trajectory inference

Step-by-Step Methodology

Data preparation - Standardize gene/protein identifiers (convert to Entrez, Ensembl, or UniProt IDs as needed). Remove duplicates and handle ambiguous mappings. Verify organism and genome build.
Differential analysis - For transcriptomics: DESeq2 or edgeR (count data), limma-voom (normalized). For proteomics: limma with appropriate normalization. Apply multiple testing correction (BH-FDR). Set thresholds (|log2FC| > 1, padj < 0.05 as defaults, adjustable).
Functional enrichment - Perform GO enrichment (BP, MF, CC) using clusterProfiler, g:Profiler, or DAVID. Run KEGG/Reactome pathway enrichment. Use GSEA for ranked gene lists (no arbitrary cutoff). Report enriched terms with gene ratio, p-value, adjusted p-value, and gene members.
Network analysis - Build PPI networks from STRING (confidence > 0.7 for high confidence). Identify hub genes (degree centrality), bottleneck nodes (betweenness centrality), and functional modules (MCODE, Louvain clustering). Overlay expression data on network.
Multi-omics integration - For paired omics: correlation analysis, canonical correlation (CCA), or MOFA/DIABLO. Map features across omics layers using shared identifiers or known biological connections. Identify convergent pathways.
Single-cell analysis - QC filtering (genes/cell, UMI/cell, mitochondrial %). Normalization (scran, SCTransform). Dimensionality reduction (PCA, UMAP). Clustering (Leiden, Louvain). Cell type annotation (SingleR, scType, marker genes). Trajectory inference (Monocle3, Slingshot).
Visualization - Generate volcano plots, heatmaps (with hierarchical clustering), dot plots (enrichment), network diagrams, UMAP/tSNE plots (single-cell), and circos plots (multi-omics).

Key Databases and Tools

Gene Ontology (GO) - Functional annotations
KEGG / Reactome / WikiPathways - Pathway databases
STRING / BioGRID / IntAct - PPI databases
Ensembl / NCBI / UniProt - Sequence and annotation databases
clusterProfiler / g:Profiler / DAVID - Enrichment tools
Seurat / Scanpy - Single-cell analysis frameworks
Cytoscape - Network visualization

Output Format

Enrichment results as tables: term, description, gene ratio, p-value, padj, gene list.
Volcano plots with labeled significant genes and fold-change thresholds.
Network figures with node coloring (expression), size (degree), and module highlighting.
UMAP/tSNE plots with cluster labels and cell type annotations.
Heatmaps with dendrograms and annotation bars.

Quality Checklist

Gene ID mapping verified (conversion losses reported)
Background gene set appropriate for enrichment analysis
Multiple testing correction applied (BH-FDR or equivalent)
Redundant GO terms handled (semantic similarity, REVIGO)
Network confidence threshold specified and justified
Single-cell QC thresholds documented
Batch effects assessed and corrected if present
Results cross-validated across databases or methods
Biological interpretation grounded in literature

Frequently asked questions

What does the Bioinformatics AI skill do?

Why use Bioinformatics on TypingMind?

Because you install it once and use it with any model. Bioinformatics 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 Bioinformatics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/beita6969/ScienceClaw/tree/main/skills/bioinformatics. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bioinformatics?

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 Bioinformatics?

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

Is the Bioinformatics AI skill free?

Yes. It is published on GitHub by beita6969 under the MIT license. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

When to Trigger

Step-by-Step Methodology

Key Databases and Tools

Output Format

Quality Checklist

Frequently asked questions

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