bio-clinical-databases-myvariant-queries

FreedomIntelligence/OpenClaw-Medical-SkillsBiology Medicine and BioinformaticsAudit passedSnapshot aaa219a3d423

Summary

Query myvariant.info API for aggregated variant annotations from multiple databases (ClinVar, gnomAD, dbSNP, COSMIC, etc.) in a single request. Use when annotating variants with clinical and population data from multiple sources simultaneously.

SKILL.md

Version Compatibility

Reference examples tested with: SnpEff 5.2+, pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

MyVariant.info Queries

"Annotate my variants from multiple databases at once" → Query the myvariant.info aggregation API to retrieve ClinVar, gnomAD, dbSNP, COSMIC, and other annotations in a single request per variant.

Python: myvariant.MyVariantInfo().getvariants(ids, fields='clinvar,gnomad,dbnsfp')

Required Imports

import myvariant

Initialize Client

mv = myvariant.MyVariantInfo()

Query Single Variant

Goal: Retrieve aggregated annotations for a single variant from multiple databases in one request.

Approach: Query myvariant.info by HGVS notation or rsID, which returns ClinVar, gnomAD, dbSNP, COSMIC, and CADD data.

# 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

Goal: Batch-query up to 1000 variants in a single API call with field selection for efficiency.

Approach: Pass a list of variant identifiers to getvariants() with specific field filters to minimize response size.

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 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')

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

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')

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)

# 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

bio-clinical-databases-myvariant-queries

Summary

SKILL.md

Version Compatibility

MyVariant.info Queries

Required Imports

Initialize Client

Query Single Variant

Query Multiple Variants

Search Variants

Available Fields

Extract Specific Annotations

Batch Processing with DataFrame

Rate Limiting

Related Skills

Related skills