MyVariant.info Queries
Required Imports
python
import myvariant
Initialize Client
python
mv = myvariant.MyVariantInfo()
Query Single Variant
python
# Query by HGVS notation (recommended)
result = mv.getvariant('chr7:g.140453136A>T')
# Query by rsID
result = mv.getvariant('rs121913527')
# Query by gene and protein change
result = mv.getvariant('BRAF:p.V600E')
Query Multiple Variants
python
variants = [
'chr7:g.140453136A>T',
'chr17:g.7577120C>T',
'rs121913527'
]
# Batch query (up to 1000 variants per request)
results = mv.getvariants(variants)
# With specific fields
results = mv.getvariants(
variants,
fields=['clinvar', 'gnomad_exome', 'dbsnp']
)
Search Variants
python
# Search by gene
results = mv.query('clinvar.gene.symbol:BRCA1', size=100)
# Search pathogenic variants in gene
results = mv.query(
'clinvar.gene.symbol:BRCA1 AND clinvar.clinical_significance:Pathogenic',
size=100
)
# Search by genomic region
results = mv.query('chr7:140400000-140500000')
Available Fields
Common field paths for annotations:
| Field | Description |
|---|---|
clinvar | ClinVar annotations |
gnomad_exome | gnomAD exome frequencies |
gnomad_genome | gnomAD genome frequencies |
dbsnp | dbSNP annotations |
cosmic | COSMIC cancer mutations |
cadd | CADD deleteriousness scores |
dbnsfp | dbNSFP functional predictions |
snpeff | SnpEff annotations |
Extract Specific Annotations
python
result = mv.getvariant('chr7:g.140453136A>T')
# ClinVar classification
clinvar_sig = result.get('clinvar', {}).get('clinical_significance')
# gnomAD allele frequency
gnomad_af = result.get('gnomad_exome', {}).get('af', {}).get('af')
# CADD score
cadd_phred = result.get('cadd', {}).get('phred')
Batch Processing with DataFrame
python
import pandas as pd
variants = ['rs121913527', 'rs1800566', 'rs104894155']
results = mv.getvariants(variants, fields=['clinvar', 'gnomad_exome'])
records = []
for r in results:
records.append({
'query': r.get('query'),
'clinvar_sig': r.get('clinvar', {}).get('clinical_significance'),
'gnomad_af': r.get('gnomad_exome', {}).get('af', {}).get('af')
})
df = pd.DataFrame(records)
Rate Limiting
python
# myvariant handles rate limiting automatically
# For large batches, use chunks
def batch_query(variants, chunk_size=1000):
all_results = []
for i in range(0, len(variants), chunk_size):
chunk = variants[i:i + chunk_size]
results = mv.getvariants(chunk)
all_results.extend(results)
return all_results
Related Skills
- •clinvar-lookup - Detailed ClinVar queries
- •gnomad-frequencies - gnomAD-specific frequency queries
- •dbsnp-queries - dbSNP rsID lookups