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scientific-rare-disease-genetics

Integrate OMIM, Orphanet, DisGeNET, and IMPC data for gene-disease profiling.

3|1|Updated Feb 11, 2026
Search Tags:#genetics#rare-disease#gene-disease#orphanet#disgenet#omim#impc
One-click install
npx skills add https://github.com/nahisaho/satori --skill scientific-rare-disease-genetics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-rare-disease-genetics
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-rare-disease-genetics
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-rare-disease-genetics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

OMIM / Orphanet / DisGeNET / IMPC data are dispersed across specialized resources; this Skill unifies them into a coherent rare-disease genetics analysis pipeline that helps researchers identify candidate genes and compare human data with model organisms.

Core Features & Use Cases

  • OMIM gene-disease mapping for Mendelian links and candidate gene prioritization.
  • Orphanet classification and prevalence lookup to contextualize diseases.
  • DisGeNET disease-gene association scores to rank gene relevance.
  • IMPC mouse phenotype references for cross-species interpretation.
  • Integrated, cross-source profiling to support gene-disease hypothesis generation.

Quick Start

Run a rare-disease gene profiling workflow for a gene symbol to retrieve cross-source evidence.

Frequently Asked Questions about scientific-rare-disease-genetics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I integrate OMIM and Orphanet data for rare-disease gene profiling?▼

Rare-disease gene profiling integrates OMIM and Orphanet data by applying modular retrieval to map Mendelian links, classify diseases, and generate cross-source evidence profiles for candidate genes.

What is the best way to cross-reference DisGeNET association scores with IMPC mouse phenotypes?▼

Cross-referencing DisGeNET scores with IMPC phenotypes uses cross-source aggregation to rank gene relevance and provide model organism references, enabling cross-species interpretation for disease-gene hypotheses.

Can I use a single gene symbol to retrieve multi-source rare-disease genetics evidence?▼

Yes, querying a single gene symbol triggers a rare-disease genetics workflow that retrieves and aggregates multi-source evidence from OMIM, Orphanet, DisGeNET, and IMPC for gene-level profiling.

Do I need API keys to retrieve gene-disease associations from DisGeNET and OMIM?▼

API keys are optional for retrieving gene-disease associations; the data pipeline implements modular retrieval supporting both authenticated and unauthenticated access across the integrated rare-disease sources.

How does cross-source aggregation handle orphanet prevalence lookup during candidate gene prioritization?▼

Cross-source aggregation incorporates Orphanet prevalence lookup to contextualize diseases, merging this classification data with OMIM and DisGeNET evidence to support candidate gene prioritization.

When should I not use a multi-source rare-disease genetics pipeline for gene-disease mapping?▼

Avoid using a multi-source rare-disease genetics pipeline when your analysis requires only single-source OMIM Mendelian mapping without cross-species IMPC phenotypic data or DisGeNET association ranking.

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