Design armored CAR-T cells with cytokine payloads and resistance mechanisms.
tooluniverse-gene-enrichment
维护者 FreedomIntelligence · 最近更新 2026年4月1日
Perform comprehensive gene enrichment and pathway analysis using gseapy (ORA and GSEA), PANTHER, STRING, Reactome, and 40+ ToolUniverse tools. Supports GO enrichment (BP, MF, CC), KEGG, Reactome, WikiPathways, MSigDB Hallmark, and 220+ Enrichr libraries. Handles multiple ID types (gene symbols, Ensembl, Entrez, UniProt), multiple organisms (human, mouse, rat, fly, worm, yeast), customizable backgrounds, and multiple….
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tooluniverse-gene-enrichment
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- 执行 comprehensive gene enrichment analysis ,涵盖 Gene Ontology (GO),KEGG,Reactome,WikiPathways,、 MSigDB enrichment ,使用 both Over-Representation Analysis (ORA) 、 Gene Set Enrichment Analysis (GSEA). Integrates local computation ,通过 gseapy ,支持 ToolUniverse pathway databases ,用于 cross-validated,publication-ready results。
- IMPORTANT:Always use English terms in tool calls (gene names,pathway names,organism names),even if user writes in another language. Only try original-language terms as fallback if English returns no results. Respond in user's language。
- gene_list = ["TP53","BRCA1","EGFR"]。
原始文档
SKILL.md 摘录
When to Use This Skill
Apply when users:
- Ask about gene enrichment analysis (GO, KEGG, Reactome, etc.)
- Have a gene list from differential expression, clustering, or any experiment
- Want to know which biological processes, molecular functions, or cellular components are enriched
- Need KEGG or Reactome pathway enrichment analysis
- Ask about GSEA (Gene Set Enrichment Analysis) with ranked gene lists
- Want over-representation analysis (ORA) with Fisher's exact test
- Need multiple testing correction (Benjamini-Hochberg, Bonferroni)
- Ask about enrichGO, gseapy, clusterProfiler-style analyses
NOT for (use other skills instead):
- Network pharmacology / drug repurposing → Use
tooluniverse-network-pharmacology - Disease characterization → Use
tooluniverse-multiomic-disease-characterization - Single gene function lookup → Use
tooluniverse-disease-research - Spatial omics analysis → Use
tooluniverse-spatial-omics-analysis - Protein-protein interaction analysis only → Use
tooluniverse-protein-interactions
Input Parameters
| Parameter | Required | Description | Example |
|---|---|---|---|
| gene_list | Yes | List of gene symbols, Ensembl IDs, or Entrez IDs | ["TP53", "BRCA1", "EGFR"] |
| organism | No | Organism (default: human). Supported: human, mouse, rat, fly, worm, yeast, zebrafish | human |
| analysis_type | No | ORA (default) or GSEA | ORA |
| enrichment_databases | No | Which databases to query. Default: all applicable | ["GO_BP", "GO_MF", "GO_CC", "KEGG", "Reactome"] |
| gene_id_type | No | Input ID type: symbol, ensembl, entrez, uniprot (auto-detected if omitted) | symbol |
| p_value_cutoff | No | Significance threshold (default: 0.05) | 0.05 |
| correction_method | No | Multiple testing: BH (Benjamini-Hochberg, default), bonferroni, fdr | BH |
| background_genes | No | Custom background gene set (default: genome-wide) | ["GENE1", "GENE2", ...] |
| ranked_gene_list | No | For GSEA: gene-to-score mapping (e.g., log2FC) | {"TP53": 2.5, "BRCA1": -1.3, ...} |
适用场景
- Ask about gene enrichment analysis (GO,KEGG,Reactome,etc.)。
- Have gene list ,面向 differential expression,聚类,或 any experiment。
- Want to know which biological processes,molecular functions,或 cellular components are enriched。
- Need KEGG 或 Reactome pathway enrichment analysis。
不适用场景
- Do not rely on this catalog entry alone ,用于 installation 或 maintenance details。
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