Tooluniverse Variant Interpretation

name: tooluniverse-variant-interpretation description: Systematic clinical variant interpretation from raw variant calls to ACMG-classified recommendations with structural impact analysis. Aggregates evidence from ClinVar, gnomAD, CIViC, UniProt, and PDB across ACMG criteria. Produces pathogenicity scores (0-100), clinical recommendations, and treatment implications. Use when interpreting genetic variants, classifying variants of uncertain significance (VUS), performing ACMG variant classification, or translating variant calls to clinical actionability.

Clinical Variant Interpreter

Systematic variant interpretation skill using ToolUniverse - from raw variant calls to ACMG-classified clinical recommendations with structural impact analysis.

Problem This Skill Solves

Clinical labs and researchers face critical challenges in variant interpretation:

1. Variant classification uncertainty - VUS (Variants of Uncertain Significance) comprise 40-60% of clinical variants
2. Evidence aggregation burden - Must integrate data from 10+ databases per variant
3. Structural context missing - Traditional annotation ignores 3D protein impact
4. Clinical actionability unclear - How does classification translate to patient care?

This skill provides: A systematic workflow that combines population databases, functional predictions, structural analysis (via AlphaFold2), and literature evidence into ACMG-compliant interpretations with clear clinical recommendations.

Key Principles

1. ACMG-Guided Classification - Follow ACMG/AMP 2015 guidelines with explicit evidence codes
2. Structural Evidence Integration - Use AlphaFold2 for novel structural impact analysis
3. Population Context - gnomAD frequencies with ancestry-specific data
4. Gene-Disease Validity - ClinGen curation status for clinical relevance
5. Actionable Output - Clear recommendations, not just classifications
6. English-first queries - Always use English terms in tool calls (gene names, variant descriptions, disease names), even if the user writes in another language. Only try original-language terms as a fallback. Respond in the user's language

Triggers

Use this skill when users:

• Ask about variant interpretation or classification
• Have VCF data needing clinical annotation
• Ask "what does this variant mean clinically?"
• Need ACMG classification for variants
• Want structural impact analysis for missense variants
• Ask about pathogenicity of specific variants

Workflow Overview

┌─────────────────────────────────────────────────────────────────┐
│                    VARIANT INTERPRETATION                        │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  Phase 1: VARIANT IDENTITY                                       │
│  ├── Normalize variant notation (HGVS)                          │
│  ├── Map to gene, transcript, protein                           │
│  └── Get consequence type (missense, nonsense, etc.)            │
│                                                                  │
│  Phase 2: CLINICAL DATABASES                                     │
│  ├── ClinVar: Existing classifications                          │
│  ├── gnomAD: Population frequencies (all + ancestry)            │
│  ├── OMIM: Gene-disease associations                            │
│  ├── ClinGen: Gene validity + dosage sensitivity (ENHANCED)     │
│  │   └─ ClinGen_search_gene_validity, ClinGen_search_dosage     │
│  └── SpliceAI: Splice variant prediction (NEW)                  │
│                                                                  │
│  Phase 2.5: REGULATORY CONTEXT (NEW - for non-coding variants)  │
│  ├── ChIPAtlas: TF binding at position                          │
│  ├── ENCODE: Regulatory elements (enhancers, promoters)         │
│  ├── Conservation in regulatory regions                         │
│  └── Functional annotation of regulatory impact                 │
│                                                                  │
│  Phase 3: COMPUTATIONAL PREDICTIONS                              │
│  ├── SIFT/PolyPhen: Damaging predictions                        │
│  ├── CADD: Deleteriousness score                                │
│  ├── SpliceAI: Splice impact (if applicable)                    │
│  └── Conservation: Cross-species alignment                      │
│                                                                  │
│  Phase 4: STRUCTURAL ANALYSIS (for VUS/novel missense)          │
│  ├── Get protein structure (PDB or AlphaFold2)                  │
│  ├── Map variant to structure                                   │
│  ├── Assess domain/functional site impact                       │
│  └── Predict structural destabilization                         │
│                                                                  │
│  Phase 4.5: EXPRESSION CONTEXT (NEW)                            │
│  ├── CELLxGENE: Cell-type specific expression                   │
│  ├── Tissue relevance to phenotype                              │
│  └── Expression validation                                       │
│                                                                  │
│  Phase 5: LITERATURE EVIDENCE                                    │
│  ├── PubMed: Functional studies                                 │
│  ├── BioRxiv/MedRxiv: Recent preprints (NEW)                   │
│  ├── Case reports: Phenotype correlations                       │
│  └── Segregation data (if in literature)                        │
│                                                                  │
│  Phase 6: ACMG CLASSIFICATION                                    │
│  ├── Apply evidence codes (PVS1, PM2, PP3, etc.)               │
│  ├── Calculate classification                                   │
│  ├── Identify limiting factors                                  │
│  └── Generate clinical recommendations                          │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Phase Details

Phase 1: Variant Identity & Normalization

Goal: Standardize variant notation and determine molecular consequence

Tools:

Key Information to Capture:

• HGVS notation (c. and p.)
• Gene symbol and Ensembl ID
• Transcript (canonical/MANE Select)
• Consequence type
• Amino acid change (for missense)
• Exon/intron location

Phase 2: Clinical Database Queries

Goal: Aggregate existing clinical knowledge

Tools:

2.1 COSMIC for Somatic Context (NEW)

For cancer variants, check COSMIC for somatic mutation frequency:

def get_somatic_context(tu, gene_symbol, variant_aa):
    """Get somatic mutation context from COSMIC."""
    
    # Search for specific mutation
    cosmic = tu.tools.COSMIC_search_mutations(
        operation="search",
        terms=f"{gene_symbol} {variant_aa}",
        max_results=20,
        genome_build=38
    )
    
    # Get all gene mutations for context
    gene_mutations = tu.tools.COSMIC_get_mutations_by_gene(
        operation="get_by_gene",
        gene=gene_symbol,
        max_results=100
    )
    
    # Determine if it's a hotspot
    mutation_counts = Counter(m['MutationAA'] for m in gene_mutations.get('results', []))
    is_hotspot = variant_aa in [m[0] for m in mutation_counts.most_common(10)]
    
    return {
        'cosmic_hits': cosmic.get('results', []),
        'is_somatic_hotspot': is_hotspot,
        'cancer_types': [m['PrimarySite'] for m in cosmic.get('results', [])],
        'total_cosmic_count': cosmic.get('total_count', 0)
    }

2.2 OMIM Gene-Disease Context (NEW)

def get_omim_context(tu, gene_symbol):
    """Get OMIM gene-disease associations."""
    
    # Search OMIM for gene
    search = tu.tools.OMIM_search(
        operation="search",
        query=gene_symbol,
        limit=5
    )
    
    omim_data = []
    for entry in search.get('data', {}).get('entries', []):
        mim = entry.get('mimNumber')
        
        # Get detailed entry
        details = tu.tools.OMIM_get_entry(
            operation="get_entry",
            mim_number=str(mim)
        )
        
        # Get clinical synopsis
        synopsis = tu.tools.OMIM_get_clinical_synopsis(
            operation="get_clinical_synopsis",
            mim_number=str(mim)
        )
        
        omim_data.append({
            'mim_number': mim,
            'title': details.get('data', {}).get('titles', {}),
            'inheritance': synopsis.get('data', {}).get('inheritance'),
            'clinical_features': synopsis.get('data', {})
        })
    
    return omim_data

2.3 DisGeNET Gene-Disease Evidence (NEW)

def get_disgenet_context(tu, gene_symbol, variant_rsid=None):
    """Get gene-disease associations from DisGeNET."""
    
    # Gene-disease associations
    gda = tu.tools.DisGeNET_search_gene(
        operation="search_gene",
        gene=gene_symbol,
        limit=20
    )
    
    # Variant-disease associations (if rsID available)
    vda = None
    if variant_rsid:
        vda = tu.tools.DisGeNET_get_vda(
            operation="get_vda",
            variant=variant_rsid,
            limit=20
        )
    
    return {
        'gene_associations': gda.get('data', {}).get('associations', []),
        'variant_associations': vda.get('data', {}).get('associations', []) if vda else []
    }

2.4 ClinGen Gene Validity & Dosage Sensitivity (NEW)

ClinGen provides authoritative curation of gene-disease relationships:

def get_clingen_evidence(tu, gene_symbol):
    """
    Get ClinGen gene validity and dosage sensitivity data.
    CRITICAL for ACMG classification - establishes gene-disease validity.
    """
    
    # 1. Gene-disease validity (Definitive/Strong/Moderate/Limited)
    validity = tu.tools.ClinGen_search_gene_validity(gene=gene_symbol)
    
    validity_data = []
    if validity.get('data'):
        for entry in validity.get('data', []):
            validity_data.append({
                'disease': entry.get('Disease Label'),
                'classification': entry.get('Classification'),  # Definitive, Strong, etc.
                'inheritance': entry.get('Inheritance'),
                'mondo_id': entry.get('Disease ID (MONDO)')
            })
    
    # 2. Dosage sensitivity (haploinsufficiency, triplosensitivity)
    dosage = tu.tools.ClinGen_search_dosage_sensitivity(gene=gene_symbol)
    
    dosage_data = {}
    if dosage.get('data'):
        for entry in dosage.get('data', []):
            dosage_data = {
                'haploinsufficiency_score': entry.get('Haploinsufficiency Score'),
                'triplosensitivity_score': entry.get('Triplosensitivity Score'),
                'disease': entry.get('Disease')
            }
            break  # Usually one entry per gene
    
    # 3. Clinical actionability (for incidental findings context)
    actionability = tu.tools.ClinGen_search_actionability(gene=gene_symbol)
    
    return {
        'gene_validity': validity_data,
        'dosage_sensitivity': dosage_data,
        'actionability': actionability.get('data', {}),
        'has_definitive_validity': any(v['classification'] == 'Definitive' for v in validity_data),
        'is_haploinsufficient': dosage_data.get('haploinsufficiency_score') == '3'
    }

ClinGen Validity Levels (for ACMG PM1/PP4):

Dosage Sensitivity Scores (for CNV interpretation):

2.5 SpliceAI Splice Variant Prediction (NEW)

~15% of pathogenic variants affect splicing. SpliceAI is the gold standard for splice prediction:

def get_spliceai_prediction(tu, chrom, pos, ref, alt, genome="38"):
    """
    Get SpliceAI splice effect predictions.
    
    Delta scores:
    - DS_AG: Acceptor gain
    - DS_AL: Acceptor loss  
    - DS_DG: Donor gain
    - DS_DL: Donor loss
    
    Thresholds:
    - ≥0.8: High pathogenicity (strong PP3)
    - 0.5-0.8: Moderate (supporting PP3)
    - 0.2-0.5: Low (weak evidence)
    - <0.2: Likely benign
    """
    
    # Format variant for SpliceAI
    variant = f"chr{chrom}-{pos}-{ref}-{alt}"
    
    # Get full splice predictions
    result = tu.tools.SpliceAI_predict_splice(
        variant=variant,
        genome=genome
    )
    
    if result.get('data'):
        max_score = result['data'].get('max_delta_score', 0)
        interpretation = result['data'].get('interpretation', '')
        
        # Determine ACMG support
        if max_score >= 0.8:
            acmg = 'PP3 (strong) - high splice impact'
        elif max_score >= 0.5:
            acmg = 'PP3 (supporting) - moderate splice impact'
        elif max_score >= 0.2:
            acmg = 'PP3 (weak) - possible splice impact'
        else:
            acmg = 'BP7 (if synonymous) - splice benign'
        
        return {
            'max_delta_score': max_score,
            'interpretation': interpretation,
            'acmg_support': acmg,
            'scores': result['data'].get('scores', [])
        }
    return None

def quick_splice_check(tu, variant, genome="38"):
    """Quick triage using max delta score only."""
    
    result = tu.tools.SpliceAI_get_max_delta(
        variant=variant,
        genome=genome
    )
    
    return result.get('data', {})

When to Use SpliceAI:

• Intronic variants near splice sites (±50bp)
• Synonymous variants (may still affect splicing)
• Exonic variants near splice junctions
• Variants creating cryptic splice sites

Report Section for Splice Variants:

### Splice Impact Analysis (SpliceAI)

| Score Type | Value | Position | Interpretation |
|------------|-------|----------|----------------|
| DS_AG | 0.02 | +15 | Acceptor gain unlikely |
| DS_AL | 0.85 | -2 | **High acceptor loss** |
| DS_DG | 0.01 | +8 | Donor gain unlikely |
| DS_DL | 0.03 | +1 | Donor loss unlikely |

**Max Delta Score**: 0.85 (DS_AL)
**Interpretation**: High impact - likely disrupts acceptor site
**ACMG Support**: PP3 (strong) for splice-altering effect

*Source: SpliceAI via `SpliceAI_predict_splice`*

ClinVar Classification Map:

gnomAD Thresholds (for rare disease):

COSMIC Somatic Evidence (NEW):

DisGeNET Score Interpretation (NEW):

Phase 2.5: Regulatory Context (NEW - for Non-Coding Variants)

Goal: Assess regulatory impact for non-coding, intronic, and promoter variants

When to Apply:

• Intronic variants (not splice site)
• Promoter variants
• 5'UTR / 3'UTR variants
• Intergenic variants near disease genes

Tools:

Regulatory Impact Assessment:

def assess_regulatory_impact(tu, variant_position, gene_symbol):
    """Assess regulatory impact of non-coding variant."""
    
    # Check TF binding at position
    tf_binding = tu.tools.ChIPAtlas_enrichment_analysis(
        gene=gene_symbol,
        cell_type="all"
    )
    
    # Get ChIP-seq peaks overlapping variant
    peaks = tu.tools.ChIPAtlas_get_peak_data(
        gene=gene_symbol,
        experiment_type="TF"
    )
    
    # Search ENCODE for regulatory annotations
    encode_data = tu.tools.ENCODE_search_experiments(
        assay_title="ATAC-seq",
        biosample="all"
    )
    
    # Assess if variant disrupts TF binding
    binding_disrupted = check_motif_disruption(variant_position, peaks)
    
    return {
        'tf_binding': tf_binding,
        'regulatory_peaks': peaks,
        'encode_annotations': encode_data,
        'likely_regulatory': binding_disrupted
    }

Regulatory Impact Categories:

Output for Report:

### 2.5 Regulatory Context (for Non-Coding Variants)

| Feature | Finding | Significance |
|---------|---------|--------------|
| Variant location | Intron 5, 120bp from exon 6 | Not canonical splice |
| TF binding site | CTCF binding peak (ChIPAtlas) | May affect insulation |
| ENCODE annotation | Active enhancer (H3K27ac) | Regulatory function |
| Conservation | PhyloP = 2.8 | Moderate conservation |

**Regulatory Interpretation**: Variant overlaps CTCF binding site in active enhancer region. Potential impact on gene regulation.

*Source: ChIPAtlas, ENCODE*

Phase 3: Computational Predictions (ENHANCED)

Goal: Assess in silico pathogenicity predictions using state-of-the-art models

Tools:

3.1 CADD Deleteriousness Scoring (NEW)

def get_cadd_score(tu, chrom, pos, ref, alt):
    """Get CADD deleteriousness score for a variant."""
    
    result = tu.tools.CADD_get_variant_score(
        chrom=str(chrom),
        pos=pos,
        ref=ref,
        alt=alt,
        version="GRCh38-v1.7"
    )
    
    if result.get('status') == 'success':
        phred = result['data'].get('phred_score')
        return {
            'score': phred,
            'interpretation': result['data'].get('interpretation'),
            'acmg_support': 'PP3' if phred >= 20 else ('BP4' if phred < 15 else 'neutral')
        }
    return None

3.2 AlphaMissense Pathogenicity (NEW)

DeepMind's AlphaMissense provides state-of-the-art missense pathogenicity prediction:

def get_alphamissense_score(tu, uniprot_id, variant):
    """
    Get AlphaMissense pathogenicity score.
    variant format: 'R123H' or 'p.R123H'
    
    Thresholds:
    - Pathogenic: score > 0.564
    - Ambiguous: 0.34-0.564
    - Benign: score < 0.34
    """
    
    result = tu.tools.AlphaMissense_get_variant_score(
        uniprot_id=uniprot_id,
        variant=variant
    )
    
    if result.get('status') == 'success' and result.get('data'):
        score = result['data'].get('pathogenicity_score')
        classification = result['data'].get('classification')
        
        # Map to ACMG
        if classification == 'pathogenic':
            acmg = 'PP3 (strong)'  # AlphaMissense has high accuracy
        elif classification == 'benign':
            acmg = 'BP4 (strong)'
        else:
            acmg = 'neutral'
        
        return {
            'score': score,
            'classification': classification,
            'acmg_support': acmg
        }
    return None

3.3 EVE Evolutionary Prediction (NEW)

EVE uses unsupervised learning on evolutionary data:

def get_eve_score(tu, chrom, pos, ref, alt):
    """
    Get EVE evolutionary pathogenicity score.
    
    Threshold: >0.5 indicates likely pathogenic
    """
    
    result = tu.tools.EVE_get_variant_score(
        chrom=str(chrom),
        pos=pos,
        ref=ref,
        alt=alt
    )
    
    if result.get('status') == 'success':
        eve_scores = result['data'].get('eve_scores', [])
        if eve_scores:
            best_score = eve_scores[0]
            return {
                'score': best_score.get('eve_score'),
                'classification': best_score.get('classification'),
                'gene': best_score.get('gene_symbol'),
                'acmg_support': 'PP3' if best_score.get('eve_score', 0) > 0.5 else 'BP4'
            }
    return None

3.4 Integrated Prediction Strategy

For VUS (Variants of Uncertain Significance), combine multiple predictors:

def comprehensive_pathogenicity_assessment(tu, variant_info):
    """
    Combine all prediction tools for robust classification.
    """
    chrom = variant_info['chrom']
    pos = variant_info['pos']
    ref = variant_info['ref']
    alt = variant_info['alt']
    uniprot_id = variant_info.get('uniprot_id')
    aa_change = variant_info.get('aa_change')  # e.g., 'R123H'
    
    predictions = {}
    
    # 1. CADD (works for all variant types)
    cadd = get_cadd_score(tu, chrom, pos, ref, alt)
    if cadd:
        predictions['cadd'] = cadd
    
    # 2. AlphaMissense (missense only, requires UniProt ID)
    if uniprot_id and aa_change:
        am = get_alphamissense_score(tu, uniprot_id, aa_change)
        if am:
            predictions['alphamissense'] = am
    
    # 3. EVE (missense only)
    eve = get_eve_score(tu, chrom, pos, ref, alt)
    if eve:
        predictions['eve'] = eve
    
    # Consensus assessment
    damaging_count = sum(1 for p in predictions.values() 
                         if 'PP3' in p.get('acmg_support', ''))
    benign_count = sum(1 for p in predictions.values() 
                       if 'BP4' in p.get('acmg_support', ''))
    
    if damaging_count >= 2 and benign_count == 0:
        consensus = 'likely_damaging'
        acmg = 'PP3 (multiple predictors concordant)'
    elif benign_count >= 2 and damaging_count == 0:
        consensus = 'likely_benign'
        acmg = 'BP4 (multiple predictors concordant)'
    else:
        consensus = 'uncertain'
        acmg = 'neutral (discordant predictions)'
    
    return {
        'predictions': predictions,
        'consensus': consensus,
        'acmg_recommendation': acmg
    }

Prediction Interpretation (Updated):

ACMG Application (Enhanced):

• PP3: Multiple concordant damaging predictions (AlphaMissense + CADD + EVE agreement = strong PP3)
• BP4: Multiple concordant benign predictions
• Note: AlphaMissense alone achieves ~90% accuracy on ClinVar pathogenic variants

Phase 4: Structural Analysis

Goal: Assess protein structural impact (especially for VUS)

Tools:

Structural Impact Categories:

Using AlphaFold2 for VUS:

1. Get wildtype structure (PDB or AlphaFold)
2. Identify residue location:
   - pLDDT at position (confidence)
   - Solvent accessibility
   - Secondary structure
3. Assess structural context:
   - Distance to functional sites
   - Interaction partners
   - Conservation in structure
4. Predict impact:
   - Side chain burial
   - Hydrogen bond disruption
   - Charge changes in buried positions

Phase 4.5: Expression Context (NEW)

Goal: Validate gene expression in disease-relevant tissues/cells

Tools:

Expression Validation:

def validate_expression_context(tu, gene_symbol, phenotype_tissues):
    """Validate gene is expressed in phenotype-relevant tissues."""
    
    # Single-cell expression
    sc_expression = tu.tools.CELLxGENE_get_expression_data(
        gene=gene_symbol,
        tissue=phenotype_tissues[0] if phenotype_tissues else "all"
    )
    
    # Bulk tissue expression (GTEx)
    gtex = tu.tools.GTEx_get_median_gene_expression(
        gene=gene_symbol
    )
    
    # Check expression in relevant tissues
    relevant_expression = {
        tissue: gtex.get(tissue, 0)
        for tissue in phenotype_tissues
    }
    
    return {
        'single_cell': sc_expression,
        'gtex': relevant_expression,
        'expressed_in_phenotype_tissue': any(v > 1 for v in relevant_expression.values())
    }

Why it matters:

• Confirms gene is expressed where disease manifests
• Supports PP4 (phenotype-specific) if highly restricted expression
• Can challenge classification if not expressed in affected tissue

Output for Report:

### 4.5 Expression Context

| Tissue | Expression (TPM) | Relevance |
|--------|------------------|-----------|
| Heart | 45.2 | ✓ Primary disease tissue |
| Skeletal muscle | 38.7 | ✓ Secondary involvement |
| Liver | 2.1 | Low expression |
| Brain | 0.5 | Not expressed |

**Single-Cell Analysis (CELLxGENE)**:
- **Cardiomyocytes**: High expression (TPM=85)
- **Cardiac fibroblasts**: Low expression (TPM=5)

**Interpretation**: Gene highly expressed in cardiomyocytes, supporting cardiac phenotype association.

*Source: GTEx, CELLxGENE Census*

Phase 5: Literature Evidence (ENHANCED)

Goal: Find functional studies, case reports, and cutting-edge preprints

Tools:

Search Strategies:

def comprehensive_literature_search(tu, gene, variant, phenotype):
    """Search across all literature sources."""
    
    # 1. PubMed: Peer-reviewed
    pubmed = tu.tools.PubMed_search(
        query=f'"{gene}" AND ("{variant}" OR functional)',
        max_results=30
    )
    
    # 2. BioRxiv: Recent preprints
    biorxiv = tu.tools.BioRxiv_search_preprints(
        query=f"{gene} {phenotype}",
        limit=10
    )
    
    # 3. MedRxiv: Clinical preprints
    medrxiv = tu.tools.MedRxiv_search_preprints(
        query=f"{gene} variant {phenotype}",
        limit=10
    )
    
    # 4. Citation analysis
    key_papers = pubmed[:5]  # Top papers
    for paper in key_papers:
        citations = tu.tools.openalex_search_works(
            query=paper['title'],
            limit=1
        )
        paper['citation_count'] = citations[0].get('cited_by_count', 0) if citations else 0
    
    return {
        'pubmed': pubmed,
        'preprints': biorxiv + medrxiv,
        'key_papers_with_citations': key_papers
    }

Search Queries:

# Gene + variant specific
"{GENE} AND ({HGVS_p} OR {AA_change})"

# Functional studies
"{GENE} AND (functional OR functional study OR mutagenesis)"

# Clinical reports
"{GENE} AND (case report OR patient) AND {phenotype}"

# Preprint-specific
"{GENE} genetics 2024" (for recent preprints)

⚠️ Preprint Warning: Always flag preprints as NOT peer-reviewed in reports.

Evidence Types:

Phase 6: ACMG Classification

Goal: Systematic classification with explicit evidence

ACMG Evidence Codes:

Pathogenic:

Benign:

Classification Algorithm:

Output Structure

Report Sections

# Variant Interpretation Report: {GENE} {VARIANT}

## Executive Summary
- **Variant**: {HGVS notation}
- **Gene**: {gene symbol}
- **Classification**: {Pathogenic/Likely Pathogenic/VUS/Likely Benign/Benign}
- **Evidence Strength**: {strong/moderate/limited}
- **Key Finding**: {one-sentence summary}

## 1. Variant Identity
{gene, transcript, protein change, consequence}

## 2. Population Data
{gnomAD frequencies, ancestry breakdown}

## 3. Clinical Database Evidence
{ClinVar, ClinGen, OMIM}

## 4. Computational Predictions
{SIFT, PolyPhen, CADD scores}

## 5. Structural Analysis
{Domain location, functional site proximity, AlphaFold confidence}

## 6. Literature Evidence
{Functional studies, case reports}

## 7. ACMG Classification
{Evidence codes applied, classification rationale}

## 8. Clinical Recommendations
{Testing, management, family screening}

## 9. Limitations & Uncertainties
{Missing data, conflicting evidence}

## Data Sources
{All tools and databases queried}

Evidence Grading

Classification Confidence

Structural Impact Confidence

Special Scenarios

Scenario 1: Novel Missense VUS

Additional workflow:

1. Check if other pathogenic variants at same residue
2. Get AlphaFold2 structure
3. Analyze:
   • Is residue buried or surface?
   • What secondary structure?
   • Proximity to active/binding sites?
   • Conservation across species?
4. Apply PM1 if in functional domain
5. Apply PP3 if predictions concordant

Scenario 2: Truncating Variant

Additional workflow:

1. Check if LOF is mechanism for gene
2. Determine if escapes NMD (last exon)
3. Check for alternative isoforms
4. Review ClinGen LOF curation

PVS1 Application:

Scenario 3: Splice Variant

Additional workflow:

1. Check SpliceAI scores (if available)
2. Determine canonical splice site distance
3. Review for in-frame skipping potential
4. Check for cryptic splice activation

Quantified Minimums

NVIDIA NIM Integration

When to Use AlphaFold2 for Variants

Use Case: VUS missense variants where structural context aids interpretation

Workflow:

# 1. Get protein sequence
protein_seq = tu.tools.UniProt_get_protein_sequence(accession=uniprot_id)

# 2. Get/predict structure
try:
    pdb_hits = tu.tools.PDB_search_by_uniprot(uniprot_id=uniprot_id)
    structure = tu.tools.PDB_get_structure(pdb_id=pdb_hits[0]['pdb_id'])
except:
    # Predict with AlphaFold2
    structure = tu.tools.NvidiaNIM_alphafold2(
        sequence=protein_seq['sequence'],
        algorithm="mmseqs2"
    )

# 3. Analyze variant position
# - Extract pLDDT at residue position
# - Calculate solvent accessibility
# - Check for nearby functional sites

Structural Features to Report:

• pLDDT at variant position
• Secondary structure (helix/sheet/coil)
• Solvent accessibility (buried/exposed)
• Distance to active site (if applicable)
• Interactions disrupted (H-bonds, salt bridges)

Report File Naming

{GENE}_{VARIANT}_interpretation_report.md

Examples:
BRCA1_c.5266dupC_interpretation_report.md
TP53_p.R273H_interpretation_report.md

Clinical Recommendations Framework

For Pathogenic/Likely Pathogenic

For VUS

For Benign/Likely Benign

See Also

• CHECKLIST.md - Pre-delivery verification
• EXAMPLES.md - Sample interpretations
• TOOLS_REFERENCE.md - Tool parameters and fallbacks