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 : Report-first - Create report file FIRST, update progressively Phenotype-driven - Convert symptoms to HPO terms before searching Multi-database triangulation - Cross-reference Orphanet, OMIM, OpenTargets Evidence grading - Grade diagnoses by supporting evidence strength 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: Multi-system involvement - Symptoms spanning 2+ organ systems = strongest rare disease signal. Ask: what single pathway explains ALL features? Regression question - Losing abilities vs never acquired? Regression = neurodegenerative/metabolic storage. Stable = developmental/structural. Trigger question - Episodic/triggered (fasting, illness, exercise) = metabolic disorder (often treatable). Constitutive = structural/degenerative. Rarest feature first - Build differential from most specific finding, not most prominent. Check remaining features for consistency. Treatable-first - Move treatable conditions to top for urgent workup (enzyme replacement, dietary, chelation, vitamin-responsive). Occupational/environmental exposure - Latency up to 50 years. Asbestos/silica/heavy metals/solvents/farming. Always ask about PAST jobs. Autoimmune differential - Which joints? Symmetric? Extra-articular? Serologic pattern? Organ under attack? Rare syndrome signals - Named triads, common diagnoses failing to explain ALL findings, failed standard treatment, unusual lab findings. 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 → 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 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 - 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)