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tooluniverse-rare-disease-diagnosis▌

mims-harvard/tooluniverse · updated Apr 8, 2026

$npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-rare-disease-diagnosis
summary

Systematic diagnosis support for rare diseases using phenotype matching, gene panel prioritization, and variant interpretation across Orphanet, OMIM, HPO, ClinVar, and structure-based analysis.

skill.md

Rare Disease Diagnosis Advisor

Systematic diagnosis support for rare diseases using phenotype matching, gene panel prioritization, and variant interpretation across Orphanet, OMIM, HPO, ClinVar, and structure-based analysis.

KEY PRINCIPLES:

  1. Report-first - Create report file FIRST, update progressively
  2. Phenotype-driven - Convert symptoms to HPO terms before searching
  3. Multi-database triangulation - Cross-reference Orphanet, OMIM, OpenTargets
  4. Evidence grading - Grade diagnoses by supporting evidence strength
  5. English-first queries - Always use English terms in tool calls

LOOK UP, DON'T GUESS

When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory.

COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

Clinical Reasoning Framework (BEFORE Tools)

Apply these strategies to form a 3-5 candidate differential, then use tools to confirm/refute:

  1. Multi-system involvement - Symptoms spanning 2+ organ systems = strongest rare disease signal. Ask: what single pathway explains ALL features?
  2. Regression question - Losing abilities vs never acquired? Regression = neurodegenerative/metabolic storage. Stable = developmental/structural.
  3. Trigger question - Episodic/triggered (fasting, illness, exercise) = metabolic disorder (often treatable). Constitutive = structural/degenerative.
  4. Rarest feature first - Build differential from most specific finding, not most prominent. Check remaining features for consistency.
  5. Treatable-first - Move treatable conditions to top for urgent workup (enzyme replacement, dietary, chelation, vitamin-responsive).
  6. Occupational/environmental exposure - Latency up to 50 years. Asbestos/silica/heavy metals/solvents/farming. Always ask about PAST jobs.
  7. Autoimmune differential - Which joints? Symmetric? Extra-articular? Serologic pattern? Organ under attack?
  8. Rare syndrome signals - Named triads, common diagnoses failing to explain ALL findings, failed standard treatment, unusual lab findings.
  9. Tools verify, not generate - Form hypothesis first, then use databases to confirm.

Common pitfalls: Felty's (RA+splenomegaly+neutropenia) mimics infection; SLE nephritis mimics PSGN (check ASO); occupational exposures trigger autoimmunity (silica→scleroderma/RA/SLE).

Tool Parameter Corrections

Tool WRONG CORRECT
OpenTargets_get_associated_drugs_by_target_ensemblID ensemblID ensemblId
ClinVar_get_variant_details variant_id id
MyGene_query_genes gene q
gnomad_get_variant variant variant_id

Workflow

Phase 0: Clinical Reasoning → 3-5 candidate differential
Phase 1: Phenotype → HPO terms (HPO_search_terms), core vs variable, onset, family history
Phase 2: Disease Matching → Orphanet_search_diseases, OMIM_search, DisGeNET_search_gene
Phase 3: Gene Panel → ClinGen validation, GTEx expression, prioritization scoring
Phase 3.5: Expression Context → CELLxGENE, ChIPAtlas for tissue/cell-type confirmation
Phase 3.6: Pathway Analysis → KEGG, IntAct for convergent pathways
Phase 4: Variant Interpretation → ClinVar, gnomAD frequency, CADD/AlphaMissense/EVE/SpliceAI, ACMG criteria
Phase 5: Structure Analysis → AlphaFold2, InterPro domains (for VUS)
Phase 6: Literature → PubMed, BioRxiv/MedRxiv, OpenAlex
Phase 7: Report Synthesis → Prioritized differential with next steps

Key Phase Details

Phase 2 - Disease Matching: Orphanet_search_diseases(operation="search_diseases", query=keyword) then Orphanet_get_genes(operation="get_genes", orpha_code=code). Score overlap: Excellent >80%, Good 60-80%, Possible 40-60%.

Phase 3 - Gene Panel: ClinGen classification drives inclusion (Definitive/Strong/Moderate = include; Limited = flag; Disputed/Refuted = exclude). Scoring: Tier 1 (top disease gene +5), Tier 2 (multi-disease +3), Tier 3 (ClinGen Definitive +3), Tier 4 (tissue expression +2), Tier 5 (pLI >0.9 +1).

Phase 4 - Variants: gnomAD frequency classes: ultra-rare <0.00001, rare <0.0001, low-freq <0.01. ACMG: PVS1 (null), PS1 (same AA), PM2 (absent pop), PP3 (computational), BA1 (>5% AF). 2+ concordant predictors strengthen PP3.

Evidence Grading

Tier Criteria
T1 (High) Phenotype match >80% + gene match
T2 (Medium-High) Phenotype match 60-80% OR likely pathogenic variant
T3 (Medium) Phenotype match 40-60% OR VUS in candidate gene
T4 (Low) Phenotype <40% OR uncertain gene

Fallback Chains

Primary Fallback 1 Fallback 2
get_joint_associated_diseases_by_HPO_ID_list Orphanet_search_diseases PubMed phenotype search
ClinVar_get_variant_details gnomad_get_variant VEP annotation
GTEx_get_expression_summary HPA_search_genes_by_query Tissue-specific literature

Reference Files

  • DIAGNOSTIC_WORKFLOW.md - Code examples and algorithms per phase
  • REPORT_TEMPLATE.md - Report template and examples
  • CHECKLIST.md - Interactive completeness checklist
  • scripts/clinical_patterns.py - Clinical pattern lookup (syndromes, differentials, red flags, occupational exposures)