Agent skill
bio-clinical-databases-myvariant-queries
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.
Install this agent skill to your Project
npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-clinical-databases-myvariant-queries
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>thenhelp(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 Variants
Goal: Search for variants by gene, clinical significance, or genomic region using query syntax.
Approach: Use Lucene-style query strings with mv.query() to filter by gene symbol, ClinVar fields, or coordinate ranges.
# 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
Goal: Extract ClinVar classification, gnomAD frequency, and CADD score from a variant result.
Approach: Navigate the nested JSON response using dictionary access to reach specific annotation fields.
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
Goal: Convert batch variant query results into a structured pandas DataFrame for downstream analysis.
Approach: Query multiple rsIDs with selected fields, extract key annotations per variant, and assemble into a DataFrame.
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
Goal: Handle large variant sets exceeding the 1000-variant-per-request API limit.
Approach: Split variants into chunks and query sequentially, relying on myvariant's built-in rate limiting.
# 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
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
vcf-annotator
Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.
chemist-analyst
Analyzes events through chemistry lens using molecular structure, reaction mechanisms, thermodynamics, kinetics, and analytical techniques (spectroscopy, chromatography, mass spectrometry). Provides insights on chemical processes, material properties, reaction pathways, synthesis, and analytical methods. Use when: Chemical reactions, material analysis, synthesis planning, process optimization, environmental chemistry. Evaluates: Molecular structure, reaction mechanisms, yield, selectivity, safety, environmental impact.
bio-alignment-io
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.
sleep-analyzer
分析睡眠数据、识别睡眠模式、评估睡眠质量,并提供个性化睡眠改善建议。支持与其他健康数据的关联分析。
metabolomics-workbench-database
Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.
bio-hi-c-analysis-matrix-operations
Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.
Didn't find tool you were looking for?