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 approach - 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
Actionable output - Prioritized differential diagnosis with next steps
Genetic counseling aware - Consider inheritance patterns and family history
English-first queries - Always use English terms in tool calls (phenotype descriptions, gene names, disease names), even if the user writes in another language. Only try original-language terms as a fallback. Respond in the user's language

When to Use

Apply when user asks:

"Patient has [symptoms], what rare disease could this be?"
"Unexplained developmental delay with [features]"
"WES found VUS in [gene], is this pathogenic?"
"What genes should we test for [phenotype]?"
"Differential diagnosis for [rare symptom combination]"

Critical Workflow Requirements

1. Report-First Approach (MANDATORY)

Create the report file FIRST:
- File name: [PATIENT_ID]_rare_disease_report.md
- Initialize with all section headers
- Add placeholder text: [Researching...]
Progressively update as you gather data
Output separate data files:
- [PATIENT_ID]_gene_panel.csv - Prioritized genes for testing
- [PATIENT_ID]_variant_interpretation.csv - If variants provided

2. Citation Requirements (MANDATORY)

Every finding MUST include source:

### Candidate Disease: Marfan Syndrome
- **ORPHA**: ORPHA:558
- **OMIM**: 154700
- **Phenotype match**: 85% (17/20 HPO terms)
- **Inheritance**: AD
- **Gene**: FBN1

*Source: Orphanet via `Orphanet_558`, OMIM via `OMIM_get_entry`*

Phase 0: Tool Verification

CRITICAL: Verify tool parameters before calling.

Known Parameter Corrections

Tool	WRONG Parameter	CORRECT Parameter
`OpenTargets_get_associated_diseases_by_target_ensemblId`	`ensemblID`	`ensemblId`
`ClinVar_get_variant_by_id`	`variant_id`	`id`
`MyGene_query_genes`	`gene`	`q`
`gnomAD_get_variant_frequencies`	`variant`	`variant_id`

Workflow Overview

Phase 1: Phenotype Standardization
├── Convert symptoms to HPO terms
├── Identify core vs. variable features
└── Note age of onset, inheritance hints
    ↓
Phase 2: Disease Matching
├── Orphanet phenotype search
├── OMIM clinical synopsis match
├── OpenTargets disease associations
└── OUTPUT: Ranked differential diagnosis
    ↓
Phase 3: Gene Panel Identification
├── Extract genes from top diseases
├── Cross-reference expression (GTEx)
├── Prioritize by evidence strength
└── OUTPUT: Recommended gene panel
    ↓
Phase 3.5: Expression & Tissue Context (NEW)
├── CELLxGENE: Cell-type specific expression
├── ChIPAtlas: Regulatory context (TF binding)
├── Tissue-specific gene networks
└── OUTPUT: Expression validation
    ↓
Phase 3.6: Pathway Analysis (NEW)
├── KEGG: Metabolic/signaling pathways
├── Reactome: Biological processes
├── IntAct: Protein-protein interactions
└── OUTPUT: Biological context
    ↓
Phase 4: Variant Interpretation (if provided)
├── ClinVar pathogenicity lookup
├── gnomAD population frequency
├── Protein domain/function impact
├── ENCODE/ChIPAtlas: Regulatory variant impact
└── OUTPUT: Variant classification
    ↓
Phase 5: Structure Analysis (for VUS)
├── NvidiaNIM_alphafold2 → Predict structure
├── Map variant to structure
├── Assess functional domain impact
└── OUTPUT: Structural evidence
    ↓
Phase 6: Literature Evidence (NEW)
├── PubMed: Published studies
├── BioRxiv/MedRxiv: Preprints
├── OpenAlex: Citation analysis
└── OUTPUT: Literature support
    ↓
Phase 7: Report Synthesis
├── Prioritized differential diagnosis
├── Recommended genetic testing
├── Next steps for clinician
└── OUTPUT: Final report

Phase 1: Phenotype Standardization

1.1 Convert Symptoms to HPO Terms

def standardize_phenotype(tu, symptoms_list):
    """Convert clinical descriptions to HPO terms."""
    hpo_terms = []
    
    for symptom in symptoms_list:
        # Search HPO for matching terms
        results = tu.tools.HPO_search_terms(query=symptom)
        if results:
            hpo_terms.append({
                'original': symptom,
                'hpo_id': results[0]['id'],
                'hpo_name': results[0]['name'],
                'confidence': 'exact' if symptom.lower() in results[0]['name'].lower() else 'partial'
            })
    
    return hpo_terms

1.2 Phenotype Categories

Category	Examples	Weight
Core features	Always present in disease	High
Variable features	Present in >50%	Medium
Occasional features	Present in <50%	Low
Age-specific	Onset-dependent	Context

1.3 Output for Report

## 1. Phenotype Analysis

### 1.1 Standardized HPO Terms

| Clinical Feature | HPO Term | HPO ID | Category |
|------------------|----------|--------|----------|
| Tall stature | Tall stature | HP:0000098 | Core |
| Long fingers | Arachnodactyly | HP:0001166 | Core |
| Heart murmur | Cardiac murmur | HP:0030148 | Variable |
| Joint hypermobility | Joint hypermobility | HP:0001382 | Core |

**Total HPO Terms**: 8
**Onset**: Childhood
**Family History**: Father with similar features (AD suspected)

*Source: HPO via `HPO_search_terms`*

Phase 2: Disease Matching

2.1 Orphanet Disease Search (NEW TOOLS)

def match_diseases_orphanet(tu, symptom_keywords):
    """Find rare diseases matching symptoms using Orphanet."""
    candidate_diseases = []
    
    # Search Orphanet by disease keywords
    for keyword in symptom_keywords:
        results = tu.tools.Orphanet_search_diseases(
            operation="search_diseases",
            query=keyword
        )
        if results.get('status') == 'success':
            candidate_diseases.extend(results['data']['results'])
    
    # Get genes for each disease
    for disease in candidate_diseases:
        orpha_code = disease.get('ORPHAcode')
        genes = tu.tools.Orphanet_get_genes(
            operation="get_genes",
            orpha_code=orpha_code
        )
        disease['genes'] = genes.get('data', {}).get('genes', [])
    
    return deduplicate_and_rank(candidate_diseases)

2.2 OMIM Cross-Reference (NEW TOOLS)

def cross_reference_omim(tu, orphanet_diseases, gene_symbols):
    """Get OMIM details for diseases and genes."""
    omim_data = {}
    
    # Search OMIM for each disease/gene
    for gene in gene_symbols:
        search_result = tu.tools.OMIM_search(
            operation="search",
            query=gene,
            limit=5
        )
        if search_result.get('status') == 'success':
            for entry in search_result['data'].get('entries', []):
                mim_number = entry.get('mimNumber')
                
                # Get detailed entry
                details = tu.tools.OMIM_get_entry(
                    operation="get_entry",
                    mim_number=str(mim_number)
                )
                
                # Get clinical synopsis (phenotype features)
                synopsis = tu.tools.OMIM_get_clinical_synopsis(
                    operation="get_clinical_synopsis",
                    mim_number=str(mim_number)
                )
                
                omim_data[gene] = {
                    'mim_number': mim_number,
                    'details': details.get('data', {}),
                    'clinical_synopsis': synopsis.get('data', {})
                }
    
    return omim_data

2.3 DisGeNET Gene-Disease Associations (NEW TOOLS)

def get_gene_disease_associations(tu, gene_symbols):
    """Get gene-disease associations from DisGeNET."""
    associations = {}
    
    for gene in gene_symbols:
        # Get diseases associated with gene
        result = tu.tools.DisGeNET_search_gene(
            operation="search_gene",
            gene=gene,
            limit=20
        )
        
        if result.get('status') == 'success':
            associations[gene] = result['data'].get('associations', [])
    
    return associations

def get_disease_genes_disgenet(tu, disease_name):
    """Get all genes associated with a disease."""
    result = tu.tools.DisGeNET_search_disease(
        operation="search_disease",
        disease=disease_name,
        limit=30
    )
    return result.get('data', {}).get('associations', [])

2.4 Phenotype Overlap Scoring

Match Level	Score	Criteria
Excellent	>80%	Most core + variable features match
Good	60-80%	Core features match, some variable
Possible	40-60%	Some overlap, needs consideration
Unlikely	<40%	Poor phenotype fit

2.5 Output for Report

## 2. Differential Diagnosis

### Top Candidate Diseases (Ranked by Phenotype Match)

| Rank | Disease | ORPHA | OMIM | Match | Inheritance | Key Gene(s) |
|------|---------|-------|------|-------|-------------|-------------|
| 1 | Marfan syndrome | 558 | 154700 | 85% | AD | FBN1 |
| 2 | Loeys-Dietz syndrome | 60030 | 609192 | 72% | AD | TGFBR1, TGFBR2 |
| 3 | Ehlers-Danlos, vascular | 286 | 130050 | 65% | AD | COL3A1 |
| 4 | Homocystinuria | 394 | 236200 | 58% | AR | CBS |

### DisGeNET Gene-Disease Evidence

| Gene | Associated Diseases | GDA Score | Evidence |
|------|---------------------|-----------|----------|
| FBN1 | Marfan syndrome, MASS phenotype | 0.95 | ★★★ Curated |
| TGFBR1 | Loeys-Dietz syndrome | 0.89 | ★★★ Curated |
| COL3A1 | vascular EDS | 0.91 | ★★★ Curated |

*Source: DisGeNET via `DisGeNET_search_gene`*

### Disease Details

#### 1. Marfan Syndrome (★★★)

**ORPHA**: 558 | **OMIM**: 154700 | **Prevalence**: 1-5/10,000

**Phenotype Match Analysis**:
| Patient Feature | Disease Feature | Match |
|-----------------|-----------------|-------|
| Tall stature | Present in 95% | ✓ |
| Arachnodactyly | Present in 90% | ✓ |
| Joint hypermobility | Present in 85% | ✓ |
| Cardiac murmur | Aortic root dilation (70%) | Partial |

**OMIM Clinical Synopsis** (via `OMIM_get_clinical_synopsis`):
- **Cardiovascular**: Aortic root dilation, mitral valve prolapse
- **Skeletal**: Scoliosis, pectus excavatum, tall stature
- **Ocular**: Ectopia lentis, myopia

**Diagnostic Criteria**: Ghent nosology (2010)
- Aortic root dilation/dissection + FBN1 mutation = Diagnosis
- Without genetic testing: systemic score ≥7 + ectopia lentis

**Inheritance**: Autosomal dominant (25% de novo)

*Source: Orphanet via `Orphanet_get_disease`, OMIM via `OMIM_get_entry`, DisGeNET*

Phase 3: Gene Panel Identification

3.1 Extract Disease Genes

def build_gene_panel(tu, candidate_diseases):
    """Build prioritized gene panel from candidate diseases."""
    genes = {}
    
    for disease in candidate_diseases:
        for gene in disease['genes']:
            if gene not in genes:
                genes[gene] = {
                    'symbol': gene,
                    'diseases': [],
                    'evidence_level': 'unknown'
                }
            genes[gene]['diseases'].append(disease['name'])
    
    return genes

3.1.1 ClinGen Gene-Disease Validity Check (NEW)

Critical: Always verify gene-disease validity through ClinGen before including in panel.

def get_clingen_gene_evidence(tu, gene_symbol):
    """
    Get ClinGen gene-disease validity and dosage sensitivity.
    ESSENTIAL for rare disease gene panel prioritization.
    """
    
    # 1. Gene-disease validity classification
    validity = tu.tools.ClinGen_search_gene_validity(gene=gene_symbol)
    
    validity_levels = []
    diseases_with_validity = []
    if validity.get('data'):
        for entry in validity.get('data', []):
            validity_levels.append(entry.get('Classification'))
            diseases_with_validity.append({
                'disease': entry.get('Disease Label'),
                'mondo_id': entry.get('Disease ID (MONDO)'),
                'classification': entry.get('Classification'),
                'inheritance': entry.get('Inheritance')
            })
    
    # 2. Dosage sensitivity (critical for CNV interpretation)
    dosage = tu.tools.ClinGen_search_dosage_sensitivity(gene=gene_symbol)
    
    hi_score = None
    ts_score = None
    if dosage.get('data'):
        for entry in dosage.get('data', []):
            hi_score = entry.get('Haploinsufficiency Score')
            ts_score = entry.get('Triplosensitivity Score')
            break
    
    # 3. Clinical actionability (return of findings context)
    actionability = tu.tools.ClinGen_search_actionability(gene=gene_symbol)
    is_actionable = (actionability.get('adult_count', 0) > 0 or 
                     actionability.get('pediatric_count', 0) > 0)
    
    # Determine best evidence level
    level_priority = ['Definitive', 'Strong', 'Moderate', 'Limited', 'Disputed', 'Refuted']
    best_level = 'Not curated'
    for level in level_priority:
        if level in validity_levels:
            best_level = level
            break
    
    return {
        'gene': gene_symbol,
        'evidence_level': best_level,
        'diseases_curated': diseases_with_validity,
        'haploinsufficiency_score': hi_score,
        'triplosensitivity_score': ts_score,
        'is_actionable': is_actionable,
        'include_in_panel': best_level in ['Definitive', 'Strong', 'Moderate']
    }

def prioritize_genes_with_clingen(tu, gene_list):
    """Prioritize genes using ClinGen evidence levels."""
    
    prioritized = []
    for gene in gene_list:
        evidence = get_clingen_gene_evidence(tu, gene)
        
        # Score based on ClinGen classification
        score = 0
        if evidence['evidence_level'] == 'Definitive':
            score = 5
        elif evidence['evidence_level'] == 'Strong':
            score = 4
        elif evidence['evidence_level'] == 'Moderate':
            score = 3
        elif evidence['evidence_level'] == 'Limited':
            score = 1
        # Disputed/Refuted get 0
        
        # Bonus for haploinsufficiency score 3
        if evidence['haploinsufficiency_score'] == '3':
            score += 1
        
        # Bonus for actionability
        if evidence['is_actionable']:
            score += 1
        
        prioritized.append({
            **evidence,
            'priority_score': score
        })
    
    # Sort by priority score
    return sorted(prioritized, key=lambda x: x['priority_score'], reverse=True)

ClinGen Classification Impact on Panel:

Classification	Include in Panel?	Priority
Definitive	YES - mandatory	Highest
Strong	YES - highly recommended	High
Moderate	YES	Medium
Limited	Include but flag	Low
Disputed	Exclude or separate	Avoid
Refuted	EXCLUDE	Do not test
Not curated	Use other evidence	Variable

3.2 Gene Prioritization Criteria

Priority	Criteria	Points
Tier 1	Gene causes #1 ranked disease	+5
Tier 2	Gene causes multiple candidates	+3
Tier 3	ClinGen "Definitive" evidence	+3
Tier 4	Expressed in affected tissue	+2
Tier 5	Constraint score pLI >0.9	+1

3.3 Expression Validation

def validate_expression(tu, gene_symbol, affected_tissue):
    """Check if gene is expressed in relevant tissue."""
    # Get Ensembl ID
    gene_info = tu.tools.MyGene_query_genes(q=gene_symbol, species="human")
    ensembl_id = gene_info.get('ensembl', {}).get('gene')
    
    # Check GTEx expression
    expression = tu.tools.GTEx_get_median_gene_expression(
        gencode_id=f"{ensembl_id}.latest"
    )
    
    return expression.get(affected_tissue, 0) > 1  # TPM > 1

3.4 Output for Report

## 3. Recommended Gene Panel

### 3.1 Prioritized Genes for Testing

| Priority | Gene | Diseases | Evidence | Constraint (pLI) | Expression |
|----------|------|----------|----------|------------------|------------|
| ★★★ | FBN1 | Marfan syndrome | Definitive | 1.00 | Heart, aorta |
| ★★★ | TGFBR1 | Loeys-Dietz 1 | Definitive | 0.98 | Ubiquitous |
| ★★★ | TGFBR2 | Loeys-Dietz 2 | Definitive | 0.99 | Ubiquitous |
| ★★☆ | COL3A1 | EDS vascular | Definitive | 1.00 | Connective tissue |
| ★☆☆ | CBS | Homocystinuria | Definitive | 0.00 | Liver |

### 3.2 Panel Design Recommendation

**Minimum Panel** (high yield): FBN1, TGFBR1, TGFBR2, COL3A1
**Extended Panel** (+differential): Add CBS, SMAD3, ACTA2

**Testing Strategy**:
1. Start with FBN1 sequencing (highest pre-test probability)
2. If negative, proceed to full connective tissue panel
3. Consider WES if panel negative

*Source: ClinGen via gene-disease validity, GTEx expression*

Phase 3.5: Expression & Tissue Context (ENHANCED)

3.5.1 Cell-Type Specific Expression (CELLxGENE)

def get_cell_type_expression(tu, gene_symbol, affected_tissues):
    """Get single-cell expression to validate tissue relevance."""
    
    # Get expression across cell types
    expression = tu.tools.CELLxGENE_get_expression_data(
        gene=gene_symbol,
        tissue=affected_tissues[0] if affected_tissues else "all"
    )
    
    # Get cell type metadata
    cell_metadata = tu.tools.CELLxGENE_get_cell_metadata(
        gene=gene_symbol
    )
    
    # Identify high-expression cell types
    high_expression = [
        ct for ct in expression 
        if ct.get('mean_expression', 0) > 1.0  # TPM > 1
    ]
    
    return {
        'expression_data': expression,
        'high_expression_cells': high_expression,
        'total_cell_types': len(cell_metadata)
    }

Why it matters: Confirms candidate genes are expressed in disease-relevant tissues/cells.

3.5.2 Regulatory Context (ChIPAtlas)

def get_regulatory_context(tu, gene_symbol):
    """Get transcription factor binding for candidate genes."""
    
    # Search for TF binding near gene
    tf_binding = tu.tools.ChIPAtlas_enrichment_analysis(
        gene=gene_symbol,
        cell_type="all"
    )
    
    # Get specific binding peaks
    peaks = tu.tools.ChIPAtlas_get_peak_data(
        gene=gene_symbol,
        experiment_type="TF"
    )
    
    return {
        'transcription_factors': tf_binding,
        'regulatory_peaks': peaks
    }

Why it matters: Identifies regulatory mechanisms that may be disrupted in disease.

3.5.3 Output for Report

## 3.5 Expression & Regulatory Context

### Cell-Type Specific Expression (CELLxGENE)

| Gene | Top Expressing Cell Types | Expression Level | Tissue Relevance |
|------|---------------------------|------------------|------------------|
| FBN1 | Fibroblasts, Smooth muscle | High (TPM=45) | ✓ Connective tissue |
| TGFBR1 | Endothelial, Fibroblasts | Medium (TPM=12) | ✓ Vascular |
| COL3A1 | Fibroblasts, Myofibroblasts | Very High (TPM=120) | ✓ Connective tissue |

**Interpretation**: All top candidate genes show high expression in disease-relevant cell types (connective tissue, vascular cells), supporting their candidacy.

### Regulatory Context (ChIPAtlas)

| Gene | Key TF Regulators | Regulatory Significance |
|------|-------------------|------------------------|
| FBN1 | TGFβ pathway (SMAD2/3), AP-1 | TGFβ-responsive |
| TGFBR1 | STAT3, NF-κB | Inflammation-responsive |

*Source: CELLxGENE Census, ChIPAtlas*

Phase 3.6: Pathway Analysis (NEW)

3.6.1 KEGG Pathway Context

def get_pathway_context(tu, gene_symbols):
    """Get pathway context for candidate genes."""
    
    pathways = {}
    for gene in gene_symbols:
        # Search KEGG for gene
        kegg_genes = tu.tools.kegg_find_genes(query=f"hsa:{gene}")
        
        if kegg_genes:
            # Get pathway membership
            gene_info = tu.tools.kegg_get_gene_info(gene_id=kegg_genes[0]['id'])
            pathways[gene] = gene_info.get('pathways', [])
    
    return pathways

3.6.2 Protein-Protein Interactions (IntAct)

def get_protein_interactions(tu, gene_symbol):
    """Get interaction partners for candidate genes."""
    
    # Search IntAct for interactions
    interactions = tu.tools.intact_search_interactions(
        query=gene_symbol,
        species="human"
    )
    
    # Get interaction network
    network = tu.tools.intact_get_interaction_network(
        gene=gene_symbol,
        depth=1  # Direct interactors only
    )
    
    return {
        'interactions': interactions,
        'network': network,
        'interactor_count': len(interactions)
    }

3.6.3 Output for Report

## 3.6 Pathway & Network Context

### KEGG Pathways

| Gene | Key Pathways | Biological Process |
|------|--------------|-------------------|
| FBN1 | ECM-receptor interaction (hsa04512) | Extracellular matrix |
| TGFBR1/2 | TGF-beta signaling (hsa04350) | Cell signaling |
| COL3A1 | Focal adhesion (hsa04510) | Cell-matrix adhesion |

### Shared Pathway Analysis

**Convergent pathways** (≥2 candidate genes):
- TGF-beta signaling pathway: FBN1, TGFBR1, TGFBR2, SMAD3
- ECM organization: FBN1, COL3A1

**Interpretation**: Candidate genes converge on TGF-beta signaling and extracellular matrix pathways, consistent with connective tissue disorder etiology.

### Protein-Protein Interactions (IntAct)

| Gene | Direct Interactors | Notable Partners |
|------|-------------------|------------------|
| FBN1 | 42 | LTBP1, TGFB1, ADAMTS10 |
| TGFBR1 | 68 | TGFBR2, SMAD2, SMAD3 |

*Source: KEGG, IntAct, Reactome*

Phase 4: Variant Interpretation (If Provided)

4.1 ClinVar Lookup

def interpret_variant(tu, variant_hgvs):
    """Get ClinVar interpretation for variant."""
    result = tu.tools.ClinVar_search_variants(query=variant_hgvs)
    
    return {
        'clinvar_id': result.get('id'),
        'classification': result.get('clinical_significance'),
        'review_status': result.get('review_status'),
        'conditions': result.get('conditions'),
        'last_evaluated': result.get('last_evaluated')
    }

4.2 Population Frequency

def check_population_frequency(tu, variant_id):
    """Get gnomAD allele frequency."""
    freq = tu.tools.gnomAD_get_variant_frequencies(variant_id=variant_id)
    
    # Interpret rarity
    if freq['allele_frequency'] < 0.00001:
        rarity = "Ultra-rare"
    elif freq['allele_frequency'] < 0.0001:
        rarity = "Rare"
    elif freq['allele_frequency'] < 0.01:
        rarity = "Low frequency"
    else:
        rarity = "Common (likely benign)"
    
    return freq, rarity

4.3 Computational Pathogenicity Prediction (ENHANCED)

Use state-of-the-art prediction tools for VUS interpretation:

def comprehensive_vus_prediction(tu, variant_info):
    """
    Combine multiple prediction tools for VUS classification.
    Critical for rare disease variants not in ClinVar.
    """
    predictions = {}
    
    # 1. CADD - Deleteriousness (NEW API)
    cadd = tu.tools.CADD_get_variant_score(
        chrom=variant_info['chrom'],
        pos=variant_info['pos'],
        ref=variant_info['ref'],
        alt=variant_info['alt'],
        version="GRCh38-v1.7"
    )
    if cadd.get('status') == 'success':
        predictions['cadd'] = {
            'score': cadd['data'].get('phred_score'),
            'interpretation': cadd['data'].get('interpretation'),
            'acmg': 'PP3' if cadd['data'].get('phred_score', 0) >= 20 else 'neutral'
        }
    
    # 2. AlphaMissense - DeepMind pathogenicity (NEW)
    if variant_info.get('uniprot_id') and variant_info.get('aa_change'):
        am = tu.tools.AlphaMissense_get_variant_score(
            uniprot_id=variant_info['uniprot_id'],
            variant=variant_info['aa_change']  # e.g., "E1541K"
        )
        if am.get('status') == 'success' and am.get('data'):
            classification = am['data'].get('classification')
            predictions['alphamissense'] = {
                'score': am['data'].get('pathogenicity_score'),
                'classification': classification,
                'acmg': 'PP3 (strong)' if classification == 'pathogenic' else (
                    'BP4 (strong)' if classification == 'benign' else 'neutral'
                )
            }
    
    # 3. EVE - Evolutionary prediction (NEW)
    eve = tu.tools.EVE_get_variant_score(
        chrom=variant_info['chrom'],
        pos=variant_info['pos'],
        ref=variant_info['ref'],
        alt=variant_info['alt']
    )
    if eve.get('status') == 'success':
        eve_scores = eve['data'].get('eve_scores', [])
        if eve_scores:
            predictions['eve'] = {
                'score': eve_scores[0].get('eve_score'),
                'classification': eve_scores[0].get('classification'),
                'acmg': 'PP3' if eve_scores[0].get('eve_score', 0) > 0.5 else 'BP4'
            }
    
    # 4. SpliceAI - Splice variant prediction (NEW)
    # Use for intronic, synonymous, or exonic variants near splice sites
    variant_str = f"chr{variant_info['chrom']}-{variant_info['pos']}-{variant_info['ref']}-{variant_info['alt']}"
    splice = tu.tools.SpliceAI_predict_splice(
        variant=variant_str,
        genome="38"
    )
    if splice.get('data'):
        max_score = splice['data'].get('max_delta_score', 0)
        interpretation = splice['data'].get('interpretation', '')
        
        if max_score >= 0.8:
            splice_acmg = 'PP3 (strong) - high splice impact'
        elif max_score >= 0.5:
            splice_acmg = 'PP3 (moderate) - splice impact'
        elif max_score >= 0.2:
            splice_acmg = 'PP3 (supporting) - possible splice effect'
        else:
            splice_acmg = 'BP7 (if synonymous) - no splice impact'
        
        predictions['spliceai'] = {
            'max_delta_score': max_score,
            'interpretation': interpretation,
            'scores': splice['data'].get('scores', []),
            'acmg': splice_acmg
        }
    
    # Consensus for PP3/BP4
    damaging = sum(1 for p in predictions.values() if 'PP3' in p.get('acmg', ''))
    benign = sum(1 for p in predictions.values() if 'BP4' in p.get('acmg', ''))
    
    return {
        'predictions': predictions,
        'consensus': {
            'damaging_count': damaging,
            'benign_count': benign,
            'pp3_applicable': damaging >= 2 and benign == 0,
            'bp4_applicable': benign >= 2 and damaging == 0
        }
    }

4.4 ACMG Classification Criteria

Evidence Type	Criteria	Weight
PVS1	Null variant in gene where LOF is mechanism	Very Strong
PS1	Same amino acid change as established pathogenic	Strong
PM2	Absent from population databases	Moderate
PP3	Computational evidence supports deleterious (AlphaMissense, CADD, EVE, SpliceAI)	Supporting
BA1	Allele frequency >5%	Benign standalone

Enhanced PP3 Evidence (NEW):

AlphaMissense pathogenic (>0.564) = Strong PP3 support (~90% accuracy)
CADD ≥20 + EVE >0.5 = Multiple concordant predictions
Agreement from 2+ predictors strengthens PP3 evidence

4.5 Output for Report

## 4. Variant Interpretation

### 4.1 Variant: FBN1 c.4621G>A (p.Glu1541Lys)

| Property | Value | Interpretation |
|----------|-------|----------------|
| Gene | FBN1 | Marfan syndrome gene |
| Consequence | Missense | Amino acid change |
| ClinVar | VUS | Uncertain significance |
| gnomAD AF | 0.000004 | Ultra-rare (PM2) |

### 4.2 Computational Predictions (NEW)

| Predictor | Score | Classification | ACMG Support |
|-----------|-------|----------------|--------------|
| **AlphaMissense** | 0.78 | Pathogenic | PP3 (strong) |
| **CADD PHRED** | 28.5 | Top 0.1% deleterious | PP3 |
| **EVE** | 0.72 | Likely pathogenic | PP3 |

**Consensus**: 3/3 predictors concordant damaging → **Strong PP3 support**

*Source: AlphaMissense, CADD API, EVE via Ensembl VEP*

### 4.3 ACMG Evidence Summary

| Criterion | Evidence | Strength |
|-----------|----------|----------|
| PM2 | Absent from gnomAD (AF < 0.00001) | Moderate |
| PP3 | AlphaMissense + CADD + EVE concordant | Supporting (strong) |
| PP4 | Phenotype highly specific for Marfan | Supporting |
| PS4 | Multiple affected family members | Strong |

**Preliminary Classification**: Likely Pathogenic (1 Strong + 1 Moderate + 2 Supporting)

*Source: ClinVar, gnomAD, AlphaMissense, CADD, EVE*

Phase 5: Structure Analysis for VUS

5.1 When to Perform Structure Analysis

Perform when:

Variant is VUS or conflicting interpretations
Missense variant in critical domain
Novel variant not in databases
Additional evidence needed for classification

5.2 Structure Prediction (NVIDIA NIM)

def analyze_variant_structure(tu, protein_sequence, variant_position):
    """Predict structure and analyze variant impact."""
    
    # Predict structure with AlphaFold2
    structure = tu.tools.NvidiaNIM_alphafold2(
        sequence=protein_sequence,
        algorithm="mmseqs2",
        relax_prediction=False
    )
    
    # Extract pLDDT at variant position
    variant_plddt = get_residue_plddt(structure, variant_position)
    
    # Check if in structured region
    confidence = "High" if variant_plddt > 70 else "Low"
    
    return {
        'structure': structure,
        'variant_plddt': variant_plddt,
        'confidence': confidence
    }

5.3 Domain Impact Assessment

def assess_domain_impact(tu, uniprot_id, variant_position):
    """Check if variant affects functional domain."""
    
    # Get domain annotations
    domains = tu.tools.InterPro_get_protein_domains(accession=uniprot_id)
    
    for domain in domains:
        if domain['start'] <= variant_position <= domain['end']:
            return {
                'in_domain': True,
                'domain_name': domain['name'],
                'domain_function': domain['description']
            }
    
    return {'in_domain': False}

5.4 Output for Report

## 5. Structural Analysis

### 5.1 Structure Prediction

**Method**: AlphaFold2 via NVIDIA NIM
**Protein**: Fibrillin-1 (FBN1)
**Sequence Length**: 2,871 amino acids

| Metric | Value | Interpretation |
|--------|-------|----------------|
| Mean pLDDT | 85.3 | High confidence overall |
| Variant position pLDDT | 92.1 | Very high confidence |
| Nearby domain | cbEGF-like domain 23 | Calcium-binding |

### 5.2 Variant Location Analysis

**Variant**: p.Glu1541Lys

| Feature | Finding | Impact |
|---------|---------|--------|
| Domain | cbEGF-like domain 23 | Critical for calcium binding |
| Conservation | 100% conserved across vertebrates | High constraint |
| Structural role | Calcium coordination residue | Likely destabilizing |
| Nearby pathogenic | p.Glu1540Lys (Pathogenic) | Adjacent residue |

### 5.3 Structural Interpretation

The variant p.Glu1541Lys:
1. **Located in cbEGF domain** - These domains are critical for fibrillin-1 function
2. **Glutamate → Lysine** - Charge reversal (negative to positive)
3. **Calcium binding** - Glutamate at this position coordinates Ca2+
4. **Adjacent pathogenic variant** - p.Glu1540Lys is classified Pathogenic

**Structural Evidence**: Strong support for pathogenicity (PM1 - critical domain)

*Source: NVIDIA NIM via `NvidiaNIM_alphafold2`, InterPro*

Phase 6: Literature Evidence (NEW)

6.1 Published Literature (PubMed)

def search_disease_literature(tu, disease_name, genes):
    """Search for relevant published literature."""
    
    # Disease-specific search
    disease_papers = tu.tools.PubMed_search_articles(
        query=f'"{disease_name}" AND (genetics OR mutation OR variant)',
        limit=20
    )
    
    # Gene-specific searches
    gene_papers = []
    for gene in genes[:5]:  # Top 5 genes
        papers = tu.tools.PubMed_search_articles(
            query=f'"{gene}" AND rare disease AND pathogenic',
            limit=10
        )
        gene_papers.extend(papers)
    
    return {
        'disease_literature': disease_papers,
        'gene_literature': gene_papers
    }

6.2 Preprint Literature (BioRxiv/MedRxiv)

def search_preprints(tu, disease_name, genes):
    """Search preprints for cutting-edge findings."""
    
    # BioRxiv search
    biorxiv = tu.tools.BioRxiv_search_preprints(
        query=f"{disease_name} genetics",
        limit=10
    )
    
    # ArXiv for computational methods
    arxiv = tu.tools.ArXiv_search_papers(
        query=f"rare disease diagnosis {' OR '.join(genes[:3])}",
        category="q-bio",
        limit=5
    )
    
    return {
        'biorxiv': biorxiv,
        'arxiv': arxiv
    }

6.3 Citation Analysis (OpenAlex)

def analyze_citations(tu, key_papers):
    """Analyze citation network for key papers."""
    
    citation_analysis = []
    for paper in key_papers[:5]:
        # Get citation data
        work = tu.tools.openalex_search_works(
            query=paper['title'],
            limit=1
        )
        if work:
            citation_analysis.append({
                'title': paper['title'],
                'citations': work[0].get('cited_by_count', 0),
                'year': work[0].get('publication_year')
            })
    
    return citation_analysis

6.4 Output for Report

## 6. Literature Evidence

### 6.1 Key Published Studies

| PMID | Title | Year | Citations | Relevance |
|------|-------|------|-----------|-----------|
| 32123456 | FBN1 variants in Marfan syndrome... | 2023 | 45 | Direct |
| 31987654 | TGF-beta signaling in connective... | 2022 | 89 | Pathway |
| 30876543 | Novel diagnostic criteria for... | 2021 | 156 | Diagnostic |

### 6.2 Recent Preprints (Not Yet Peer-Reviewed)

| Source | Title | Posted | Relevance |
|--------|-------|--------|-----------|
| BioRxiv | Novel FBN1 splice variant causes... | 2024-01 | Case report |
| MedRxiv | Machine learning for Marfan... | 2024-02 | Diagnostic |

**⚠️ Note**: Preprints have not undergone peer review. Use with caution.

### 6.3 Evidence Summary

| Evidence Type | Count | Strength |
|---------------|-------|----------|
| Case reports | 12 | Supporting |
| Functional studies | 5 | Strong |
| Clinical trials | 2 | Strong |
| Reviews | 8 | Context |

*Source: PubMed, BioRxiv, OpenAlex*

Report Template

File: [PATIENT_ID]_rare_disease_report.md

# Rare Disease Diagnostic Report

**Patient ID**: [ID] | **Date**: [Date] | **Status**: In Progress

---

## Executive Summary
[Researching...]

---

## 1. Phenotype Analysis
### 1.1 Standardized HPO Terms
[Researching...]
### 1.2 Key Clinical Features
[Researching...]

---

## 2. Differential Diagnosis
### 2.1 Ranked Candidate Diseases
[Researching...]
### 2.2 Disease Details
[Researching...]

---

## 3. Recommended Gene Panel
### 3.1 Prioritized Genes
[Researching...]
### 3.2 Testing Strategy
[Researching...]

---

## 4. Variant Interpretation (if applicable)
### 4.1 Variant Details
[Researching...]
### 4.2 ACMG Classification
[Researching...]

---

## 5. Structural Analysis (if applicable)
### 5.1 Structure Prediction
[Researching...]
### 5.2 Variant Impact
[Researching...]

---

## 6. Clinical Recommendations
### 6.1 Diagnostic Next Steps
[Researching...]
### 6.2 Specialist Referrals
[Researching...]
### 6.3 Family Screening
[Researching...]

---

## 7. Data Gaps & Limitations
[Researching...]

---

## 8. Data Sources
[Will be populated as research progresses...]

Evidence Grading

Tier	Symbol	Criteria	Example
T1	★★★	Phenotype match >80% + gene match	Marfan with FBN1 mutation
T2	★★☆	Phenotype match 60-80% OR likely pathogenic variant	Good phenotype fit
T3	★☆☆	Phenotype match 40-60% OR VUS in candidate gene	Possible diagnosis
T4	☆☆☆	Phenotype <40% OR uncertain gene	Low probability

Completeness Checklist

Phase 1: Phenotype

All symptoms converted to HPO terms
Core vs. variable features distinguished
Age of onset documented
Family history noted

Phase 2: Disease Matching

≥5 candidate diseases identified (or all matching)
Phenotype overlap % calculated
Inheritance patterns noted
ORPHA and OMIM IDs provided

Phase 3: Gene Panel

≥5 genes prioritized (or all from top diseases)
Evidence level for each gene (ClinGen)
Expression validation performed
Testing strategy recommended

Phase 4: Variant Interpretation (if applicable)

ClinVar classification retrieved
gnomAD frequency checked
ACMG criteria applied
Classification justified

Phase 5: Structure Analysis (if applicable)

Structure predicted (if VUS)
pLDDT confidence reported
Domain impact assessed
Structural evidence summarized

Phase 6: Recommendations

≥3 next steps listed
Specialist referrals suggested
Family screening addressed

Fallback Chains

Primary Tool	Fallback 1	Fallback 2
`Orphanet_search_by_hpo`	`OMIM_search`	PubMed phenotype search
`ClinVar_get_variant`	`gnomAD_get_variant`	VEP annotation
`NvidiaNIM_alphafold2`	`alphafold_get_prediction`	UniProt features
`GTEx_expression`	`HPA_expression`	Tissue-specific literature
`gnomAD_get_variant`	`ExAC_frequencies`	1000 Genomes

Tool Reference

See TOOLS_REFERENCE.md for complete tool documentation.

tooluniverse-rare-disease-diagnosis

Rare Disease Diagnosis Advisor

When to Use

Critical Workflow Requirements

1. Report-First Approach (MANDATORY)

2. Citation Requirements (MANDATORY)

Phase 0: Tool Verification

Known Parameter Corrections

Workflow Overview

Phase 1: Phenotype Standardization

1.1 Convert Symptoms to HPO Terms

1.2 Phenotype Categories

1.3 Output for Report

Phase 2: Disease Matching

2.1 Orphanet Disease Search (NEW TOOLS)

2.2 OMIM Cross-Reference (NEW TOOLS)

2.3 DisGeNET Gene-Disease Associations (NEW TOOLS)

2.4 Phenotype Overlap Scoring

2.5 Output for Report

Phase 3: Gene Panel Identification

3.1 Extract Disease Genes

3.1.1 ClinGen Gene-Disease Validity Check (NEW)

3.2 Gene Prioritization Criteria

3.3 Expression Validation

3.4 Output for Report

Phase 3.5: Expression & Tissue Context (ENHANCED)

3.5.1 Cell-Type Specific Expression (CELLxGENE)

3.5.2 Regulatory Context (ChIPAtlas)

3.5.3 Output for Report

Phase 3.6: Pathway Analysis (NEW)

3.6.1 KEGG Pathway Context

3.6.2 Protein-Protein Interactions (IntAct)

3.6.3 Output for Report

Phase 4: Variant Interpretation (If Provided)

4.1 ClinVar Lookup

4.2 Population Frequency

4.3 Computational Pathogenicity Prediction (ENHANCED)

4.4 ACMG Classification Criteria

4.5 Output for Report

Phase 5: Structure Analysis for VUS

5.1 When to Perform Structure Analysis

5.2 Structure Prediction (NVIDIA NIM)

5.3 Domain Impact Assessment

5.4 Output for Report

Phase 6: Literature Evidence (NEW)

6.1 Published Literature (PubMed)

6.2 Preprint Literature (BioRxiv/MedRxiv)

6.3 Citation Analysis (OpenAlex)

6.4 Output for Report

Report Template

Evidence Grading

Completeness Checklist

Phase 1: Phenotype

Phase 2: Disease Matching

Phase 3: Gene Panel

Phase 4: Variant Interpretation (if applicable)

Phase 5: Structure Analysis (if applicable)

Phase 6: Recommendations

Fallback Chains

Tool Reference