From mims-harvard-tooluniverse
Explores KEGG disease-drug-variant relationships using Disease, Drug, Network, and Variant databases for gene lookups, drug-target analysis, network exploration, and variant annotation.
npx claudepluginhub joshuarweaver/cascade-data-analytics --plugin mims-harvard-tooluniverseThis skill uses the workspace's default tool permissions.
Systematic exploration of disease-drug-variant relationships using KEGG's curated databases.
Conducts multi-round deep research on GitHub repos via API and web searches, generating markdown reports with executive summaries, timelines, metrics, and Mermaid diagrams.
Dynamically discovers and combines enabled skills into cohesive, unexpected delightful experiences like interactive HTML or themed artifacts. Activates on 'surprise me', inspiration, or boredom cues.
Generates images from structured JSON prompts via Python script execution. Supports reference images and aspect ratios for characters, scenes, products, visuals.
Systematic exploration of disease-drug-variant relationships using KEGG's curated databases.
KEGG maps diseases to pathways and drugs to targets, but the real value is in the connections — which pathways link a disease gene to a drug target? This is a network question, not a simple lookup. A gene appearing in a KEGG disease entry has been editorially reviewed as mechanistically relevant; a drug entry with a confirmed target is more reliable than one inferred from pathway co-membership. When using KEGG for drug repurposing, always ask: is the drug-target relationship direct (the drug binds the gene product) or indirect (the drug affects a pathway that contains the gene)? Direct relationships are far stronger evidence. KEGG coverage is not exhaustive — absence from KEGG does not mean absence of biological involvement; complement with Reactome, WikiPathways, or CTD for broader coverage. ID namespace differences are a frequent source of errors: KEGG uses its own gene IDs (e.g., hsa:7157 for TP53), so always convert external IDs before querying KEGG-specific tools.
LOOK UP DON'T GUESS: Do not assume KEGG disease IDs, drug IDs, or gene IDs from memory — always search first with KEGG_search_disease, KEGG_search_drug, or KEGG_convert_ids. Do not assume which pathways link a disease gene to a drug; use KEGG_link_entries and KEGG_get_network to retrieve the actual connections.
| Tool | Key Params | Returns |
|---|---|---|
| KEGG_search_disease | keyword | Disease entries matching keyword |
| KEGG_get_disease | disease_id (e.g., "H00004") | Disease details: genes, drugs, pathways |
| KEGG_get_disease_genes | disease_id | All genes for a disease |
| KEGG_search_drug | keyword | Drug entries matching keyword |
| KEGG_get_drug | drug_id (e.g., "D00123") | Drug details: targets, pathways, metabolism |
| KEGG_get_drug_targets | drug_id | Molecular targets for a drug |
| KEGG_search_network | keyword | Network entries (disease-gene-drug) |
| KEGG_get_network | network_id | Network details and relationships |
| KEGG_search_variant | keyword | Variant entries matching keyword |
| KEGG_get_variant | variant_id | Variant details and disease associations |
| KEGG_convert_ids | source_db, target_db, ids | Convert identifiers between KEGG and external databases (e.g., NCBI Gene ↔ KEGG gene IDs, UniProt ↔ KEGG) |
| KEGG_link_entries | target_db, source_db_or_ids | Find cross-database relationships (e.g., all genes linked to a pathway, all drugs linked to a disease) |
Phase 1: Disease Lookup -> Phase 2: Disease Genes -> Phase 3: Drug Search
-> Phase 4: Drug Targets -> Phase 5: Network/Variant Context -> Report
Search and retrieve KEGG disease entries.
# Search for cancer-related diseases
diseases = tu.tools.KEGG_search_disease(keyword="breast cancer")
# Get details for a specific disease
disease = tu.tools.KEGG_get_disease(disease_id="H00031")
Get genes associated with a KEGG disease entry.
genes = tu.tools.KEGG_get_disease_genes(disease_id="H00031")
Find KEGG drugs by name, target, or keyword.
drugs = tu.tools.KEGG_search_drug(keyword="vemurafenib")
drug_detail = tu.tools.KEGG_get_drug(drug_id="D09996")
Get molecular targets for a drug.
targets = tu.tools.KEGG_get_drug_targets(drug_id="D09996")
Explore disease-gene-drug networks and variant annotations.
# Search networks linking disease, genes, and drugs
networks = tu.tools.KEGG_search_network(keyword="BRAF melanoma")
network = tu.tools.KEGG_get_network(network_id="N00001")
# Search and get variant details
variants = tu.tools.KEGG_search_variant(keyword="BRAF V600E")
variant = tu.tools.KEGG_get_variant(variant_id="hsa:BRAF")
from tooluniverse import ToolUniverse
tu = ToolUniverse()
tu.load_tools()
# 1. Find BRAF-related diseases
diseases = tu.tools.KEGG_search_disease(keyword="BRAF")
# 2. Get disease genes for melanoma
genes = tu.tools.KEGG_get_disease_genes(disease_id="H00038")
# 3. Search for BRAF-targeting drugs
drugs = tu.tools.KEGG_search_drug(keyword="BRAF inhibitor")
# 4. Get targets for vemurafenib
targets = tu.tools.KEGG_get_drug_targets(drug_id="D09996")
# 5. Get BRAF variant info
variants = tu.tools.KEGG_search_variant(keyword="BRAF V600E")
# 6. Explore disease-gene-drug network
networks = tu.tools.KEGG_search_network(keyword="BRAF melanoma")
Use KEGG_convert_ids to map between KEGG identifiers and external databases before or after lookups:
# Convert NCBI Gene IDs to KEGG gene IDs for human (hsa)
result = tu.tools.KEGG_convert_ids(source_db="ncbi-geneid", target_db="hsa", ids=["672", "675"])
# Convert UniProt accessions to KEGG entries
result = tu.tools.KEGG_convert_ids(source_db="up", target_db="hsa", ids=["P38398"])
Use KEGG_link_entries to retrieve relationships between KEGG databases:
# Find all KEGG pathway IDs that contain a given gene
result = tu.tools.KEGG_link_entries(target_db="pathway", source_db_or_ids="hsa:7157")
# Find all genes linked to a specific pathway
result = tu.tools.KEGG_link_entries(target_db="hsa", source_db_or_ids="path:hsa05210")
These tools are especially useful when you have external IDs (Entrez Gene, UniProt, ChEMBL) and need to bridge into KEGG's namespace, or when you want a complete gene-pathway or drug-disease adjacency list.
tooluniverse-systems-biology for Reactome/WikiPathways cross-reftooluniverse-drug-mechanism-research for ChEMBL/DailyMed MOAtooluniverse-cancer-variant-interpretation for CIViC/ClinVartooluniverse-adverse-event-detection for FAERS data| Grade | Criteria | Example |
|---|---|---|
| Strong | KEGG disease entry with curated gene list, drug with confirmed target, pathway mechanistically linked | H00031 (breast cancer) with BRCA1/BRCA2 genes, D09996 (vemurafenib) targeting BRAF |
| Moderate | Disease-gene link in KEGG but no drug-target validation, or network entry without variant data | KEGG disease entry lists gene, but drug targets are inferred from pathway membership |
| Weak | Keyword search hit only, no curated disease-gene-drug relationship in KEGG | Drug found by name search but not linked to the disease in KEGG network |
| Insufficient | No KEGG entries found, or only cross-database ID conversion available | Rare disease not curated in KEGG Disease |
KEGG_convert_ids to map from external IDs (NCBI Gene, UniProt) before querying KEGG-specific tools. Failed conversions may indicate the gene is not in KEGG's curated set.Markdown report with: