monarch-database
Monarch Initiative knowledge graph REST API for disease-gene-phenotype associations and cross-species orthology. MONDO disease-to-gene/phenotype, HP phenotype profiles, cross-species comparisons. Use for rare disease gene prioritization and phenotype-based candidate ranking. For GWAS use gwas-database; for clinical pathogenicity use clinvar-database.
npx skills add BioTender-max/awesome-bio-agent-skills --skill monarch-database --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Retrieves disease-gene and disease-phenotype associations, patient phenotype-driven candidate ranking, cross-species gene-phenotype data, and entity metadata using the Monarch API. Includes examples for querying disease-to-gene, disease-to-phenotype, and gene-to-phenotype relationships, plus entity lookup and text search.
How it works
The skill describes multiple API queries against the Monarch API at /v3/api to:
- Retrieve all genes associated with a disease (subject MONDO ID) using the category biolink:CausalGeneToDiseaseAssociation and extract fields like gene_id, gene_symbol, taxon, relation, and evidence_count.
- Retrieve HPO phenotype terms for a disease (category biolink:DiseaseToPhenotypicFeatureAssociation) and extract hp_id, phenotype, frequency, and onset.
- Retrieve entity metadata by ID via /entity/{entity_id} and extract name, id, description, and synonyms.
- Perform free-text searches via /search with optional category to resolve names to IDs.
- Retrieve diseases linked to a gene (category biolink:GeneToDiseaseAssociation) and extract disease_id, disease_name, and predicate.
- Retrieve gene-phenotype associations cross-species (category biolink:GeneToPhenotypicFeatureAssociation) and extract gene_id, gene_symbol, taxon, phenotype_id, and phenotype.
- Retrieve histopheno data for a disease via /histopheno/{mondo_id} and interpret item labels and counts.
- Retrieve phenotype-to-gene associations given a HP term (category biolink:GeneToPhenotypicFeatureAssociation) and extract gene_id, gene_symbol, and taxon.
- The content includes code blocks illustrating Python usage with requests, pandas, and helper functions, demonstrating how to call monarch_get and process results.
When to use it
- Mapping a MONDO disease to associated causal genes and evidence sources
- Retrieving phenotype profiles (HP terms) for a disease
- Ranking candidate genes by phenotypic similarity to patient HPO terms
- Querying cross-species gene-phenotype associations for model organism comparisons
- Exploring rare disease gene-phenotype networks for diagnostic candidate generation
- Resolving entity metadata from MONDO/HP/HGNC IDs
- If drug-target evidence is needed, use opentargets-database; for clinical pathogenicity, use clinvar-database
What it can touch
- Uses REST API endpoints from Monarch Initiative. Requires internet connection; no API key is required for academic use. Prerequisites include Python packages: requests, pandas, matplotlib.
Caveats
- Rate limiting guidance is provided (sleep between batch requests, avoid bursts). The API is described as free for academic use; no authentication is mentioned.
- The skill emphasizes concrete endpoints and response fields but does not guarantee any particular data volume or specific results; users should handle empty results gracefully.
# monarch-database ## Overview The Monarch Initiative integrates disease-phenotype-gene relationships from 30+ biomedical databases (OMIM, Orphanet, ClinVar, MGI, ZFIN, Reactome) into a unified knowledge graph. The REST API at `https://api.monarchinitiative.org/v3/api` provides access to associations between genes, diseases, and phenotypes using MONDO disease IDs, Human Phenotype Ontology (HPO) terms, and standard gene identifiers. No authentication is required; the service is free for academic use. ## When to Use - Mapping a disease (MONDO ID) to all associated causal genes and their evidence sources - Retrieving phenotype profiles (HP terms) for a disease to build phenotypic similarity models - Ranking candidate genes by phenotypic similarity to a patient's HPO symptom list - Querying cross-species gene-phenotype associations (mouse, zebrafish, fly) for model organism comparisons - Exploring rare disease gene-phenotype networks for diagnostic candidate generation - Resolving entity metadata (gene symbol, disease name, phenotype label) from a MONDO/HP/HGNC ID - Use `opentargets-database` instead when you need drug-target evidence scores or tractability data alongside disease assoc
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Query 1: Disease-Gene Associations
- Query 2: Disease-Phenotype Associations
- Query 3: Entity Lookup
- Query 4: Text Search for Entities
- Query 5: Gene-to-Disease Associations
- Query 6: Gene-Phenotype Associations (Cross-Species)
- Query 7: Histopheno — Phenotype Distribution for a Disease
- Query 8: Phenotype-to-Gene Associations
- Key Concepts
pip install requests pandas matplotlib
What does the monarch-database skill do?
Monarch Initiative knowledge graph REST API for disease-gene-phenotype associations and cross-species orthology. MONDO disease-to-gene/phenotype, HP phenotype profiles, cross-species comparisons. Use for rare disease gene prioritization and phenotype-based candidate ranking. For GWAS use gwas-database; for clinical pathogenicity use clinvar-database.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill monarch-database --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.