tooluniverse-rare-disease-diagnosis

name: tooluniverse-rare-disease-diagnosis description: Rare disease differential diagnosis from patient phenotype — HPO term matching to candidate diseases (Orphanet, OMIM), gene panel prioritization, ACMG variant interpretation, and structure-based variant analysis. Use for diagnostic odyssey assistance, phenotype-to-disease ranking, and genetic-counseling differential generation. disable-model-invocation: true

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

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 → MARRVEL_get_gene (aggregated IDs) + MARRVEL_get_omim_phenotypes (OMIM disease+inheritance), 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 → FAVOR_annotate_variant (one-call: freq + CADD/SIFT/PolyPhen/AlphaMissense + ClinVar + conservation), then ClinVar, gnomAD frequency, 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: For each candidate gene, MARRVEL_get_gene(symbol) resolves OMIM/HGNC/Ensembl/Entrez/UniProt IDs in one call, and MARRVEL_get_omim_phenotypes(symbol) lists the Mendelian diseases linked to the gene with mode of inheritance — use the inheritance pattern to filter candidates against the pedigree (e.g. drop AR genes for a clearly dominant pedigree). Then 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: Start with FAVOR_annotate_variant("chr-pos-ref-alt") (GRCh38) for a single-call snapshot — population frequencies (gnomAD by ancestry, BRAVO), GENCODE consequence, CADD/SIFT/PolyPhen-2/AlphaMissense scores, conservation, and ClinVar significance — then drill into ClinVar/gnomAD/EVE/SpliceAI for detail. 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

Fallback Chains

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)