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-counse

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 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 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 MARRVEL get omim phenotypes OMIM search Orphanet gene disease FAVOR annotate variant ClinVar get variant details gnomad get variant 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](DIAGNOSTIC WORKFLOW.md) Code examples and algorithms per phase [REPORT TEMPLATE.md](REPORT TEMPLATE.md) Report template and examples [CHECKLIST.md](CHECKLIST.md) Interactive completeness checklist scripts/clinical patterns.py Clinical pattern lookup (syndromes, differentials, red flags, occupational exposures)

tooluniverse-rare-disease-diagnosis