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Clinical Variant Interpreter - Tool Reference

Core Annotation Tools

MyVariant.info - Aggregated Annotations

Tool Purpose Key Parameters
myvariant_query Query variant annotations variant_id, fields

Example - Query variant:

result = tu.tools.myvariant_query(
    variant_id="chr17:g.7674220C>T",
    fields="clinvar,gnomad,cadd,dbnsfp"
)
# Returns: ClinVar, gnomAD, CADD, dbNSFP predictions

Key Fields:

Field Contains
clinvar Classification, review status
gnomad Allele frequencies
cadd CADD scores
dbnsfp SIFT, PolyPhen, REVEL, etc.

ClinVar - Clinical Classifications

Tool Purpose Key Parameters
clinvar_search Search by variant variant, gene
clinvar_get_variant Get by VCV ID variation_id

Example - Search ClinVar:

result = tu.tools.clinvar_search(
    variant="NM_007294.4:c.5266dupC"
)
# Returns: VCV ID, classification, review status, submitters

Classification Interpretation:

ClinVar Status Stars Meaning
Expert panel ★★★★ Highest confidence
Practice guideline ★★★★ Clinical standard
Multiple submitters, criteria ★★★ Well-supported
Single submitter, criteria ★★ Limited evidence
Single submitter, no criteria ★ Minimal support

gnomAD - Population Frequencies

Tool Purpose Key Parameters
gnomad_search Get allele frequencies variant, dataset

Example - Query gnomAD:

result = tu.tools.gnomad_search(
    variant="17-7674220-C-T"
)
# Returns: AF, ancestry-specific AFs, AC, AN, homozygotes

ACMG Frequency Thresholds:

Frequency Code Application
>5% BA1 Benign (stand-alone)
>1% BS1 Strong benign
Absent PM2 Supporting pathogenic

Ancestry-Specific Populations:

Code Population
nfe European (Non-Finnish)
fin Finnish
afr African/African American
amr Latino/Admixed American
eas East Asian
sas South Asian
asj Ashkenazi Jewish

ClinGen - Gene Validity & Dosage Sensitivity (NEW)

Authoritative curation of gene-disease relationships from ClinGen.

Gene Validity

Tool Purpose Key Parameters
ClinGen_search_gene_validity Search validity by gene gene
ClinGen_get_gene_validity Get all validity curations gene (optional filter)

Example - Check gene-disease validity:

result = tu.tools.ClinGen_search_gene_validity(gene="BRCA1")
# Returns: Classification (Definitive/Strong/Moderate/Limited), disease, inheritance

Validity Classification Interpretation:

Classification ACMG Impact Usage
Definitive Supports PS4, PP4 Strong gene-disease evidence
Strong Supports PP4 Good evidence for classification
Moderate Supports PP4 (weak) Use with caution
Limited Do not apply PP4 Insufficient evidence
Disputed/Refuted Contra-evidence Gene likely NOT causative

Dosage Sensitivity

Tool Purpose Key Parameters
ClinGen_search_dosage_sensitivity HI/TS scores by gene gene
ClinGen_get_dosage_sensitivity All dosage curations gene, include_regions

Example - Check haploinsufficiency:

result = tu.tools.ClinGen_search_dosage_sensitivity(gene="MECP2")
# Returns: Haploinsufficiency Score (0-3), Triplosensitivity Score (0-3)

Dosage Score Interpretation (for CNVs):

Score Meaning Usage
3 Sufficient evidence HI/TS established - PVS1 for LOF CNVs
2 Emerging evidence Some support
1 Little evidence Minimal support
0/40 No evidence / Dosage unlikely Unknown or unlikely dosage effect

Clinical Actionability

Tool Purpose Key Parameters
ClinGen_search_actionability Actionability by gene (both contexts) gene
ClinGen_get_actionability_adult Adult actionability gene (optional)
ClinGen_get_actionability_pediatric Pediatric actionability gene (optional)

Why ClinGen is Critical:

  • Required for PP4 (phenotype specificity)
  • Establishes gene-disease validity before classification
  • Dosage scores critical for PVS1 in CNV interpretation
  • Actionability informs return of incidental findings

SpliceAI - Splice Variant Prediction (NEW)

Deep learning model for predicting splice-altering effects.

Tool Purpose Key Parameters
SpliceAI_predict_splice Full splice prediction variant, genome, distance, mask
SpliceAI_get_max_delta Quick max score variant, genome
SpliceAI_predict_pangolin Pangolin model (alternative) variant, genome

Example - Predict splice effect:

# Full prediction
result = tu.tools.SpliceAI_predict_splice(
    variant="chr17-41276045-A-G",
    genome="38"
)
# Returns: DS_AG, DS_AL, DS_DG, DS_DL scores, max_delta_score, interpretation

# Quick triage
quick = tu.tools.SpliceAI_get_max_delta(
    variant="chr17-41276045-A-G"
)
# Returns: max_delta_score, interpretation

Variant Format: chr{chrom}-{pos}-{ref}-{alt} or {chrom}:{pos}:{ref}:{alt}

Delta Score Types:

Score Meaning
DS_AG Acceptor Gain (creates new acceptor)
DS_AL Acceptor Loss (disrupts existing)
DS_DG Donor Gain (creates new donor)
DS_DL Donor Loss (disrupts existing)

Score Interpretation for ACMG:

Max Delta Score Interpretation ACMG Support
≥0.8 High splice impact PP3 (strong)
0.5-0.8 Moderate impact PP3 (supporting)
0.2-0.5 Low impact PP3 (weak)
<0.2 Likely no impact BP7 (if synonymous)

When to Use:

  • Intronic variants within ±50bp of splice sites
  • Synonymous/missense variants (may still affect splicing)
  • Deep intronic variants creating cryptic splice sites
  • Validation when functional studies suggest splice defect

Pathogenicity Prediction Tools (NEW)

CADD - Combined Annotation Dependent Depletion (NEW API)

Tool Purpose Key Parameters
CADD_get_variant_score Get PHRED score for variant chrom, pos, ref, alt, version
CADD_get_position_scores All substitutions at position chrom, pos
CADD_get_range_scores Scores in genomic range (max 100bp) chrom, start, end

Example - Score a variant:

result = tu.tools.CADD_get_variant_score(
    chrom="17",
    pos=7674220,
    ref="G",
    alt="A",
    version="GRCh38-v1.7"  # Options: GRCh38-v1.7, GRCh37-v1.7
)
# Returns: phred_score, raw_score, interpretation

CADD PHRED Score Interpretation:

Score Meaning ACMG Support
≥30 Top 0.1% deleterious PP3 (strong)
≥20 Top 1% deleterious PP3 (supporting)
15-20 Uncertain Neutral
<15 Likely benign BP4 (supporting)

AlphaMissense - DeepMind Pathogenicity Prediction (NEW)

State-of-the-art deep learning model for missense pathogenicity prediction.

Tool Purpose Key Parameters
AlphaMissense_get_variant_score Score specific variant uniprot_id, variant
AlphaMissense_get_residue_scores All substitutions at position uniprot_id, position

Example - Get pathogenicity score:

result = tu.tools.AlphaMissense_get_variant_score(
    uniprot_id="P00533",  # EGFR
    variant="L858R"  # or "p.L858R"
)
# Returns: pathogenicity_score, classification

AlphaMissense Thresholds (from Cheng et al., Science 2023):

Score Classification ACMG Support
>0.564 Pathogenic PP3 (strong)
0.34-0.564 Ambiguous Neutral
<0.34 Benign BP4 (strong)

Why Use AlphaMissense:

  • ~90% accuracy on ClinVar pathogenic variants
  • Covers all ~71 million possible human missense variants
  • Trained on evolutionary data, not clinical annotations

EVE - Evolutionary Variant Effect (NEW)

Unsupervised deep learning model using evolutionary data (Harvard/Oxford).

Tool Purpose Key Parameters
EVE_get_variant_score Get EVE score via VEP chrom, pos, ref, alt OR variant (HGVS)
EVE_get_gene_info Check if gene has EVE coverage gene_symbol

Example - Score variant:

# Via genomic coordinates
result = tu.tools.EVE_get_variant_score(
    chrom="17",
    pos=7674220,
    ref="C",
    alt="T"
)

# Or via HGVS
result = tu.tools.EVE_get_variant_score(
    variant="ENST00000269305.4:c.100G>A"
)
# Returns: eve_score, classification, gene, polyphen/sift from VEP

EVE Score Interpretation:

Score Classification ACMG Support
>0.5 Likely pathogenic PP3
≤0.5 Likely benign BP4

Note: EVE covers ~3,000 disease-related genes. Use EVE_get_gene_info to check coverage.

Integrating Prediction Tools

Best Practice for VUS Classification:

def get_multi_predictor_evidence(tu, variant_info):
    """
    Combine multiple predictors for robust PP3/BP4 assignment.
    """
    evidence = []
    
    # 1. CADD (all variants)
    cadd = tu.tools.CADD_get_variant_score(
        chrom=variant_info['chrom'],
        pos=variant_info['pos'],
        ref=variant_info['ref'],
        alt=variant_info['alt']
    )
    if cadd.get('status') == 'success':
        score = cadd['data']['phred_score']
        evidence.append({
            'tool': 'CADD',
            'score': score,
            'damaging': score >= 20
        })
    
    # 2. AlphaMissense (missense only)
    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']
        )
        if am.get('status') == 'success' and am.get('data'):
            evidence.append({
                'tool': 'AlphaMissense',
                'score': am['data'].get('pathogenicity_score'),
                'classification': am['data'].get('classification'),
                'damaging': am['data'].get('classification') == 'pathogenic'
            })
    
    # 3. EVE (via VEP)
    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:
            evidence.append({
                'tool': 'EVE',
                'score': eve_scores[0].get('eve_score'),
                'damaging': eve_scores[0].get('eve_score', 0) > 0.5
            })
    
    # Consensus
    damaging = sum(1 for e in evidence if e.get('damaging'))
    benign = sum(1 for e in evidence if not e.get('damaging'))
    
    return {
        'predictions': evidence,
        'damaging_count': damaging,
        'benign_count': benign,
        'acmg_pp3': damaging >= 2 and benign == 0,
        'acmg_bp4': benign >= 2 and damaging == 0
    }

Somatic & Disease Association Tools (NEW)

COSMIC - Somatic Cancer Mutations

Tool Purpose Key Parameters
COSMIC_search_mutations Search mutations operation="search", terms, max_results
COSMIC_get_mutations_by_gene Gene mutations operation="get_by_gene", gene, genome_build

Example - Check if variant is somatic hotspot:

# Search for specific mutation
result = tu.tools.COSMIC_search_mutations(
    operation="search",
    terms="BRAF V600E",
    max_results=20
)
# Returns: mutation_id, cancer types, frequency

# Get all mutations for gene (hotspot analysis)
gene_muts = tu.tools.COSMIC_get_mutations_by_gene(
    operation="get_by_gene",
    gene="BRAF",
    max_results=200
)
# Returns: All mutations with cancer type distribution

COSMIC Evidence for ACMG:

Finding ACMG Code Application
Recurrent somatic hotspot PS3 Functional evidence
Frequent in COSMIC (>100) PM1 Hotspot/functional domain
Rare in COSMIC - Consider other evidence

OMIM - Mendelian Disease Context

⚠️ Requires: OMIM_API_KEY environment variable

Tool Purpose Key Parameters
OMIM_search Search genes/diseases operation="search", query, limit
OMIM_get_entry Detailed entry operation="get_entry", mim_number
OMIM_get_clinical_synopsis Clinical features operation="get_clinical_synopsis", mim_number
OMIM_get_gene_map Gene-disease map operation="get_gene_map", mim_number

Example - Get gene-disease context:

# Search for gene in OMIM
search = tu.tools.OMIM_search(
    operation="search",
    query="BRCA1",
    limit=5
)

# Get detailed entry with clinical info
entry = tu.tools.OMIM_get_entry(
    operation="get_entry",
    mim_number="113705"  # BRCA1
)

# Get clinical synopsis for phenotype matching
synopsis = tu.tools.OMIM_get_clinical_synopsis(
    operation="get_clinical_synopsis",
    mim_number="114480"  # Breast-ovarian cancer
)

OMIM Entry Types:

Prefix Type Example
* Gene *113705 (BRCA1)
# Phenotype with known gene #114480 (BRCA1 cancer)
% Phenotype, unknown molecular basis Mapped locus only
+ Gene and phenotype combined Historical entries

DisGeNET - Gene-Disease Associations

⚠️ Requires: DISGENET_API_KEY environment variable

Tool Purpose Key Parameters
DisGeNET_search_gene Diseases for gene operation="search_gene", gene, limit
DisGeNET_search_disease Genes for disease operation="search_disease", disease
DisGeNET_get_gda Curated associations operation="get_gda", gene, source, min_score
DisGeNET_get_vda Variant-disease operation="get_vda", variant or gene

Example - Get gene-disease evidence:

# Gene-disease associations
gda = tu.tools.DisGeNET_search_gene(
    operation="search_gene",
    gene="BRCA1",
    limit=20
)
# Returns: Associated diseases with scores

# High-confidence curated associations
curated = tu.tools.DisGeNET_get_gda(
    operation="get_gda",
    gene="BRCA1",
    source="CURATED",
    min_score=0.5
)

# Variant-disease associations
vda = tu.tools.DisGeNET_get_vda(
    operation="get_vda",
    gene="BRCA1",
    limit=30
)

DisGeNET Score for ACMG:

Score Strength ACMG Code
>0.7 Strong gene-disease PP4 (phenotype specific)
0.4-0.7 Moderate evidence Supporting
<0.4 Weak/Literature only Insufficient

Regulatory Context Tools (NEW)

ChIPAtlas - Transcription Factor Binding

Tool Purpose Key Parameters
ChIPAtlas_enrichment_analysis TF binding enrichment gene, cell_type
ChIPAtlas_get_peak_data ChIP-seq peaks gene, experiment_type
ChIPAtlas_search_datasets Find experiments antigen, cell_type

Example - Check TF binding at variant:

# Get TF binding near gene
tf_binding = tu.tools.ChIPAtlas_enrichment_analysis(
    gene="BRCA1",
    cell_type="all"
)
# Returns: TFs with binding peaks near gene

# Get specific peaks
peaks = tu.tools.ChIPAtlas_get_peak_data(
    gene="BRCA1",
    experiment_type="TF"
)

Use for: Non-coding variants that may disrupt TF binding sites

ENCODE - Regulatory Elements

Tool Purpose Key Parameters
ENCODE_search_experiments Find regulatory data assay_title, biosample
ENCODE_get_experiment Experiment details accession
ENCODE_get_biosample Sample annotations accession

Example - Get regulatory annotations:

# Search for regulatory data near variant
experiments = tu.tools.ENCODE_search_experiments(
    assay_title="ATAC-seq",
    biosample="heart"
)
# Returns: Open chromatin experiments

Key ENCODE Assays:

Assay Purpose Relevance
ATAC-seq Open chromatin Accessible regions
H3K27ac Active enhancers Regulatory activity
H3K4me3 Active promoters Promoter regions
CTCF Insulator binding Chromatin structure

Expression Context Tools (NEW)

CELLxGENE - Single-Cell Expression

Tool Purpose Key Parameters
CELLxGENE_get_expression_data Cell-type expression gene, tissue
CELLxGENE_get_cell_metadata Cell annotations gene

Example - Validate tissue expression:

# Get expression in disease-relevant tissue
expression = tu.tools.CELLxGENE_get_expression_data(
    gene="FBN1",
    tissue="heart"
)
# Returns: Expression per cell type (cardiomyocytes, fibroblasts, etc.)

Why use it: Confirms gene is expressed in phenotype-relevant cells

Literature Tools (ENHANCED)

BioRxiv/MedRxiv - Preprints

Tool Purpose Key Parameters
EuropePMC_search_articles Search preprints (bioRxiv/medRxiv) query, source='PPR', pageSize
BioRxiv_get_preprint Get preprint by DOI doi

Example - Search preprints (bioRxiv/medRxiv don't have search APIs, use EuropePMC):

# Search for recent findings
preprints = tu.tools.EuropePMC_search_articles(
    query="BRCA1 variant functional",
    source="PPR",  # PPR = Preprints only
    pageSize=10
)

⚠️ Important: Always flag preprints as NOT peer-reviewed

OpenAlex - Citation Analysis

Tool Purpose Key Parameters
openalex_search_works Search with citations query, limit

Example - Get citation counts:

# Get citation metrics for key paper
work = tu.tools.openalex_search_works(
    query="BRCA1 functional study pathogenic",
    limit=5
)
# Returns: Papers with cited_by_count, is_oa, etc.

Semantic Scholar - AI-Ranked Search

Tool Purpose Key Parameters
SemanticScholar_search_papers AI-ranked search query, limit

Example:

papers = tu.tools.SemanticScholar_search_papers(
    query="BRCA1 c.5266dupC pathogenic",
    limit=15
)

Ensembl - Variant Effect Predictor

Tool Purpose Key Parameters
Ensembl_get_variant_info VEP annotations variant_id
Ensembl_get_gene_info Gene details gene_id

Example - Get VEP data:

result = tu.tools.Ensembl_get_variant_info(
    variant_id="rs28934576"
)
# Returns: Consequence, transcript, SIFT, PolyPhen

Disease Association Tools

OMIM - Gene-Disease Relationships

Tool Purpose Key Parameters
OMIM_search Search by gene/disease query
OMIM_get_entry Get MIM entry mim_number

Example - Get OMIM associations:

result = tu.tools.OMIM_search(query="BRCA1")
# Returns: MIM#, gene-phenotype relationships, inheritance

ClinGen - Gene Validity

Tool Purpose Key Parameters
ClinGen_gene_validity Get curation status gene
ClinGen_dosage Dosage sensitivity gene

Gene Validity Levels:

Level Meaning
Definitive Strong evidence, replicated
Strong Considerable evidence
Moderate Some evidence
Limited Minimal evidence
Disputed Conflicting evidence
Refuted Evidence against

Structural Analysis Tools

PDB - Experimental Structures

Tool Purpose Key Parameters
PDB_search_by_uniprot Find structures uniprot_id
PDB_get_structure Download PDB pdb_id

Example - Get structure:

# Find PDB structures for TP53
hits = tu.tools.PDB_search_by_uniprot(uniprot_id="P04637")
if hits:
    structure = tu.tools.PDB_get_structure(pdb_id=hits[0]['pdb_id'])

AlphaFold - Predicted Structures

Tool Purpose Key Parameters
alphafold_get_prediction Get AF DB prediction accession
NvidiaNIM_alphafold2 Predict de novo sequence, algorithm

Example - Get AlphaFold structure:

# From AlphaFold DB
structure = tu.tools.alphafold_get_prediction(accession="P04637")

# Or predict de novo
structure = tu.tools.NvidiaNIM_alphafold2(
    sequence=protein_sequence,
    algorithm="mmseqs2"
)

pLDDT Interpretation:

Score Confidence Use for Variant
>90 Very high Reliable position assessment
70-90 High Reliable
50-70 Moderate Use with caution
<50 Low Likely disordered

Domain/Function Tools

Tool Purpose Key Parameters
InterPro_get_protein_domains Domain annotations accession
UniProt_get_protein_function Functional sites accession

Example - Get domains:

domains = tu.tools.InterPro_get_protein_domains(accession="P04637")
# Returns: Domain boundaries, types, functions

Literature Tools

PubMed - Literature Search

Tool Purpose Key Parameters
PubMed_search Search articles query, max_results
PubMed_get_abstract Get abstract pmid

Example - Search for functional studies:

# Gene + variant search
result = tu.tools.PubMed_search(
    query="BRCA1 AND c.5266dupC",
    max_results=10
)

# Functional studies
result = tu.tools.PubMed_search(
    query="BRCA1 AND functional study",
    max_results=20
)

Search Strategies:

Strategy Query Pattern
Specific variant "{GENE} AND ({HGVS} OR {legacy})"
Functional "{GENE} AND (functional study OR mutagenesis)"
Clinical "{GENE} AND case report AND {phenotype}"
Review "{GENE} AND review[pt]"

Workflow Code Examples

Example 1: Complete Variant Annotation

def annotate_variant(tu, variant_hgvs, gene):
    """Complete variant annotation workflow."""
    
    # Phase 1: Get aggregated annotations
    annotations = tu.tools.myvariant_query(
        variant_id=variant_hgvs,
        fields="clinvar,gnomad,cadd,dbnsfp"
    )
    
    # Phase 2: ClinVar detail
    clinvar = tu.tools.clinvar_search(variant=variant_hgvs)
    
    # Phase 3: Population frequency
    gnomad = tu.tools.gnomad_search(variant=variant_hgvs)
    
    # Phase 4: Gene context
    omim = tu.tools.OMIM_search(query=gene)
    
    # Phase 5: Literature
    literature = tu.tools.PubMed_search(
        query=f"{gene} AND {variant_hgvs}",
        max_results=20
    )
    
    return {
        'annotations': annotations,
        'clinvar': clinvar,
        'gnomad': gnomad,
        'omim': omim,
        'literature': literature
    }

Example 2: Structural Analysis for VUS

def structural_analysis_for_vus(tu, gene, uniprot_id, residue_position):
    """Structural analysis for VUS missense variants."""
    
    # Try PDB first
    pdb_structures = tu.tools.PDB_search_by_uniprot(uniprot_id=uniprot_id)
    
    if pdb_structures:
        # Use best resolution experimental structure
        best_pdb = sorted(pdb_structures, key=lambda x: x.get('resolution', 10))[0]
        structure = tu.tools.PDB_get_structure(pdb_id=best_pdb['pdb_id'])
        structure_source = f"PDB {best_pdb['pdb_id']}"
    else:
        # Fallback to AlphaFold
        structure = tu.tools.alphafold_get_prediction(accession=uniprot_id)
        structure_source = "AlphaFold DB"
    
    # Get domain information
    domains = tu.tools.InterPro_get_protein_domains(accession=uniprot_id)
    
    # Get functional sites
    functions = tu.tools.UniProt_get_protein_function(accession=uniprot_id)
    
    # Analyze residue context
    analysis = {
        'structure_source': structure_source,
        'domains': identify_domain(domains, residue_position),
        'functional_sites': find_nearby_sites(functions, residue_position),
        'pm1_applicable': assess_pm1(domains, functions, residue_position)
    }
    
    return analysis

Example 3: ACMG Classification

def calculate_acmg_classification(evidence_codes):
    """Calculate ACMG classification from evidence codes."""
    
    # Count evidence
    pathogenic = {
        'very_strong': [],
        'strong': [],
        'moderate': [],
        'supporting': []
    }
    benign = {
        'stand_alone': [],
        'strong': [],
        'supporting': []
    }
    
    for code, strength in evidence_codes:
        if code.startswith(('PVS', 'PS', 'PM', 'PP')):
            # Pathogenic evidence
            if strength == 'very_strong':
                pathogenic['very_strong'].append(code)
            elif strength == 'strong':
                pathogenic['strong'].append(code)
            elif strength == 'moderate':
                pathogenic['moderate'].append(code)
            else:
                pathogenic['supporting'].append(code)
        else:
            # Benign evidence
            if code == 'BA1':
                benign['stand_alone'].append(code)
            elif strength == 'strong':
                benign['strong'].append(code)
            else:
                benign['supporting'].append(code)
    
    # Apply ACMG rules
    if benign['stand_alone']:
        return 'Benign'
    
    if len(benign['strong']) >= 2:
        return 'Benign'
    
    vs = len(pathogenic['very_strong'])
    s = len(pathogenic['strong'])
    m = len(pathogenic['moderate'])
    p = len(pathogenic['supporting'])
    
    if (vs >= 1 and (s >= 1 or m >= 1 or m >= 2 or p >= 2)) or \
       (s >= 2) or \
       (s >= 1 and m >= 3):
        return 'Pathogenic'
    
    if (vs >= 1 and m >= 1) or \
       (s >= 1 and m >= 1 or m >= 2) or \
       (s >= 1 and p >= 2):
        return 'Likely Pathogenic'
    
    if len(benign['strong']) >= 1 and len(benign['supporting']) >= 1:
        return 'Likely Benign'
    
    return 'VUS'

Fallback Chains

Variant Annotations

Primary Fallback 1 Fallback 2
myvariant_query clinvar_search + gnomad_search Direct database queries

Structure

Primary Fallback 1 Fallback 2
PDB_search_by_uniprot alphafold_get_prediction NvidiaNIM_alphafold2

Gene Information

Primary Fallback 1 Fallback 2
OMIM_search NCBI_gene_search Ensembl_get_gene_info

Literature

Primary Fallback 1
PubMed_search EuropePMC_search

Common Parameter Mistakes

Tool Wrong Correct
myvariant_query id="rs123" variant_id="rs123"
clinvar_search gene="BRCA1:c.123" variant="NM_007294.4:c.123A>G"
gnomad_search variant="c.123A>G" variant="17-41245466-A-G"
alphafold_get_prediction uniprot="P04637" accession="P04637"

ACMG Code Quick Reference

Pathogenic Codes

Code Strength Trigger
PVS1 Very Strong Null in LOF gene
PS1 Strong Same AA as pathogenic
PS2 Strong De novo (confirmed)
PS3 Strong Functional studies
PS4 Strong Prevalence in affected
PM1 Moderate Functional domain
PM2 Moderate Absent from controls
PM3 Moderate Trans with pathogenic
PM4 Moderate Protein length change
PM5 Moderate Novel at known position
PM6 Moderate De novo (unconfirmed)
PP1 Supporting Segregation
PP2 Supporting Low missense rate gene
PP3 Supporting Computational predictions
PP4 Supporting Phenotype specific
PP5 Supporting Reputable source

Benign Codes

Code Strength Trigger
BA1 Stand-alone MAF >5%
BS1 Strong High frequency
BS2 Strong Homozygotes healthy
BS3 Strong No functional effect
BS4 Strong No segregation
BP1 Supporting Missense in LOF gene
BP2 Supporting Observed trans
BP3 Supporting In-frame, no function
BP4 Supporting Benign predictions
BP5 Supporting Alternate explanation
BP6 Supporting Reputable source
BP7 Supporting Synonymous

Quality Thresholds

Computational Predictions (Updated)

Predictor Damaging Uncertain Benign
AlphaMissense >0.564 0.34-0.564 <0.34
CADD PHRED ≥20 15-20 <15
EVE >0.5 - ≤0.5
SIFT <0.05 0.05-0.15 >0.15
PolyPhen-2 >0.85 0.15-0.85 <0.15
REVEL >0.75 0.5-0.75 <0.5

Recommended Order of Use:

  1. AlphaMissense (highest accuracy for missense, ~90%)
  2. CADD (works for all variant types)
  3. EVE (unsupervised, complements AlphaMissense)
  4. SIFT/PolyPhen (legacy, for comparison)

Concordance for PP3/BP4

Predictors Agreeing ACMG Application
All damaging (≥3) PP3 (supporting pathogenic)
All benign (≥3) BP4 (supporting benign)
Mixed Neither

Rate Limits

Tool Limit
NVIDIA NIM tools 40 RPM
PubMed 3 requests/second
Ensembl 15 requests/second

Handle with appropriate delays between calls.