Version Compatibility

Reference examples tested with: myvariant 1.0.0+, requests 2.31+, pandas 2.2+. myvariant.info aggregates >=21 sources; the operative version of each source is queryable via the _meta field and the /v1/metadata endpoint.

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. dbNSFP version drift is the dominant staleness vector: AlphaMissense was added to dbNSFP v4.4 (~2024); querying dbnsfp.alphamissense.score returns whatever version of dbNSFP is currently loaded; check _meta.src.dbnsfp.version.

MyVariant.info Queries; Aggregated Annotation

'Annotate my variants with ClinVar + gnomAD + CADD + AlphaMissense in one batch' -> Query the BioThings myvariant.info aggregator with field selection and version tracking, then parse nested responses.

• Python: myvariant.MyVariantInfo().getvariant(hgvsorrsid, fields=['clinvar', 'gnomad_exome', 'dbnsfp'])
• Python (batch): mv.getvariants(ids_list, fields=...); up to 1000 IDs per request
• Python (search): mv.query('clinvar.gene.symbol:BRCA1 AND clinvar.clinical_significance:Pathogenic')
• REST: GET https://myvariant.info/v1/variant/{hgvsorid}?fields=...
• Bulk: POST https://myvariant.info/v1/variant with comma-separated IDs

BioThings Architecture (Lelong 2022 Bioinformatics)

myvariant.info is one of three flagship BioThings APIs (with MyGene.info and MyChem.info). All three share the BioThings SDK, which auto-deploys an Elasticsearch index from heterogeneous source files via per-source dataloaders. The 2022 paper formalized the SDK; the architecture itself is older (Xin 2016 Genome Biol).

• Elasticsearch-backed: queries use Lucene operators (AND, OR, NOT, range like dbnsfp.cadd.phred:>20)
• Dotted-field-name syntax for nested JSON
• The _id field is canonical HGVS-g per record (e.g., chr7:g.117199644G>A)

Aggregated Sources: ~21 and Counting

dbNSFP is itself an aggregator. Querying dbnsfp.alphamissense.score returns the version that dbNSFP loaded, not AlphaMissense direct. The lag from publication (Cheng 2023 Science) to integration into myvariant.info is typically 6-18 months via dbNSFP.

Scopes and Query Forms

Scopes (the scopes parameter on /v1/query POST) specifies which fields to match an input ID against: hgvs, rsid, dbsnp.rsid, dbnsfp.genename, chrom, id. The id is canonical HGVS-g.

Reproducibility: The _meta Field

Every record carries _meta.src showing per-source version:

mv = myvariant.MyVariantInfo()
record = mv.getvariant('chr7:g.140453136A>T', fields=['_meta', 'clinvar', 'dbnsfp.alphamissense'])
print(record['_meta']['src']['dbnsfp']['version'])  # e.g., '4.7a'
print(record['_meta']['src']['clinvar']['version'])  # e.g., '20250901'

For reproducibility, record per-source versions in analysis output alongside results.

Comparison to Alternatives

myvariant.info does NOT produce ACMG calls; it is purely an annotation aggregator. Pair with InterVar, GeneBe, or the acmg-classification skill for classification.

Decision Tree by Query Scenario

Standard Annotation Workflow

Goal: Annotate a list of variants with the canonical clinical fields for downstream prioritization.

Approach: Batch getvariants with explicit field list; record _meta versions; convert to DataFrame.

import myvariant
import pandas as pd

mv = myvariant.MyVariantInfo()

CLINICAL_FIELDS = [
    'clinvar.clinical_significance',
    'clinvar.review_status',
    'clinvar.variant_id',
    'gnomad_exome.faf95',
    'gnomad_exome.af.af',
    'gnomad_exome.an.an',
    'gnomad_genome.faf95',
    'gnomad_genome.af.af',
    'dbsnp.rsid',
    'dbnsfp.alphamissense.score',
    'dbnsfp.alphamissense.pred',
    'dbnsfp.revel.score',
    'dbnsfp.cadd.phred',
    'dbnsfp.spliceai.master_pred',
    'dbnsfp.spliceai.ds_max',
    'cosmic.cosmic_id',
    'civic.openCravatUrl',
    '_meta'
]

def annotate_variant_list(hgvs_list):
    '''Batch-annotate variants with ClinVar / gnomAD / dbNSFP / COSMIC / CIViC fields.'''
    chunked = [hgvs_list[i:i+1000] for i in range(0, len(hgvs_list), 1000)]
    rows = []
    versions = None
    for chunk in chunked:
        results = mv.getvariants(chunk, fields=CLINICAL_FIELDS)
        for r in results:
            if versions is None and r.get('_meta'):
                versions = {src: meta.get('version') for src, meta in r['_meta'].get('src', {}).items()}
            clinvar = r.get('clinvar', {}) or {}
            gnomad_e = r.get('gnomad_exome', {}) or {}
            gnomad_g = r.get('gnomad_genome', {}) or {}
            dbnsfp = r.get('dbnsfp', {}) or {}
            faf95 = (gnomad_e.get('faf95', {}) or gnomad_g.get('faf95', {})) or {}
            rows.append({
                'variant': r.get('query'),
                'clinvar_sig': clinvar.get('clinical_significance'),
                'clinvar_review': clinvar.get('review_status'),
                'gnomad_grpmax_faf95': faf95.get('popmax'),
                'grpmax_ancestry': faf95.get('popmax_population'),
                'gnomad_af': gnomad_e.get('af', {}).get('af') or gnomad_g.get('af', {}).get('af'),
                'rsid': r.get('dbsnp', {}).get('rsid'),
                'alphamissense': dbnsfp.get('alphamissense', {}).get('score'),
                'revel': dbnsfp.get('revel', {}).get('score'),
                'cadd_phred': dbnsfp.get('cadd', {}).get('phred'),
                'spliceai_ds_max': dbnsfp.get('spliceai', {}).get('ds_max')
            })
    return pd.DataFrame(rows), versions

Elasticsearch Query Patterns

Goal: Search beyond canonical IDs; e.g., all pathogenic variants in a gene, all variants in a genomic region with CADD > 20.

Approach: Lucene syntax in mv.query(); support boolean operators, ranges, wildcards.

def find_pathogenic_in_gene(gene_symbol, max_results=500):
    '''Find ClinVar P/LP variants in a gene.'''
    query = f'clinvar.gene.symbol:{gene_symbol} AND '\
            'clinvar.clinical_significance:(Pathogenic OR "Likely pathogenic")'
    hits = mv.query(query, size=max_results, fields=['_id', 'clinvar.clinical_significance',
                                                       'clinvar.review_status'])
    return hits.get('hits', [])


def find_high_cadd_in_region(chrom, start, end, min_cadd=25):
    '''Find variants in region with CADD phred above threshold.'''
    query = f'chrom:{chrom} AND hg19.start:[{start} TO {end}] AND '\
            f'dbnsfp.cadd.phred:>{min_cadd}'
    return mv.query(query, size=500, fields=['_id', 'dbnsfp.cadd.phred', 'clinvar.clinical_significance'])


def find_alphamissense_pathogenic(gene, min_score=0.564):
    '''Find AlphaMissense pathogenic missense in a gene.

    Note: Cheng 2023 dev cutoff is 0.564 BUT this is NOT the Pejaver-style calibrated
    PP3 threshold. ClinGen has not endorsed AlphaMissense thresholds as of May 2026;
    use AlphaMissense as supporting evidence only.
    '''
    query = f'dbnsfp.genename:{gene} AND dbnsfp.alphamissense.score:>{min_score}'
    return mv.query(query, size=500, fields=['_id', 'dbnsfp.alphamissense', 'clinvar.clinical_significance'])

Per-Operation Failure Modes

1. Stale dbNSFP version

• Trigger: Query dbnsfp.alphamissense.score and trust as current.
• Mechanism: dbNSFP version lags new tools by 6-18 months; AlphaMissense (Cheng 2023) was integrated into dbNSFP 4.4 (~2024).
• Symptom: Predictions appear missing for variants newly scored by AlphaMissense.
• Fix: Check _meta.src.dbnsfp.version; for cutting-edge predictions query AlphaMissense API directly.

2. Treating AlphaMissense dev threshold as PP3-calibrated

• Trigger: Apply AlphaMissense score >0.564 as PP3 evidence.
• Mechanism: Cheng 2023 developer-recommended threshold is NOT the Pejaver 2022 calibration framework; ClinGen has not endorsed strength-graded thresholds for AlphaMissense as of May 2026.
• Symptom: Over-application of PP3 in ACMG classification.
• Fix: Treat AlphaMissense as supporting evidence; defer to clinical-databases/acmg-classification for calibrated thresholds.

3. Stacking REVEL + BayesDel + AlphaMissense as independent evidence

• Trigger: Use multiple in silico predictors as additive PP3 evidence.
• Mechanism: REVEL, BayesDel, VEST4 share ClinVar/HGMD training labels; AlphaMissense is partially independent but correlates strongly with conservation.
• Symptom: Inflated PP3 strength; double-counting.
• Fix: Apply ONE predictor per variant (Pejaver 2022 explicit recommendation).

4. Rate-limit ignorance

• Trigger: Loop sequentially over 10k variants with getvariant().
• Mechanism: ~1000 req/sec aggregate per source IP; loops trigger throttling or 429s.
• Symptom: Increasing latency, eventual failures.
• Fix: Use getvariants(chunk, fields=...) with chunk size 1000; sleep ~0.5s between chunks.

5. Field-path errors silently return None

• Trigger: Query gnomadexome.faf95.popmax but typo as gnomadexome.faf or gnomad.exomes.faf95.
• Mechanism: Elasticsearch returns null for non-existent paths; no error raised.
• Symptom: All values None; no error.
• Fix: Use print(mv.getvariant(test_id)) first to inspect actual field structure; check /v1/metadata/fields.

6. Multi-allelic rsID returns one variant only

• Trigger: Query rs12345 and treat returned variant as the variant of interest.
• Mechanism: rsID is a cluster identifier; multi-allelic clusters return multiple records.
• Symptom: Wrong allele returned for ~6-8% of rsIDs.
• Fix: Use HGVS-g instead of rsID for unambiguous lookups; for rsID queries, inspect all returned variants and filter by allele.

7. Sample overlap between sources

• Trigger: Treat ClinVar + gnomAD as independent corroboration.
• Mechanism: ClinVar variants often derive from gnomAD-included individuals; not statistically independent.
• Symptom: False sense of independent evidence in ACMG application.
• Fix: ClinVar P + gnomAD rare is operationally additive evidence per ACMG but understand the populations may overlap.

Reconciliation: When Sources Inside myvariant Disagree

Quantitative Thresholds and Conventions

Common Errors

Anticipated Reviewer Pushback

References

• Lelong S et al. 2022. BioThings SDK: a toolkit for building high-performance data APIs in biomedical research. Bioinformatics 38:2077.
• Xin J et al. 2016. High-performance web services for querying gene and variant annotation. Genome Biol 17:91.
• Cheng J et al. 2023. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 381:eadg7492.
• Pejaver V et al. 2022. Calibration of computational tools for missense variant pathogenicity classification. Am J Hum Genet 109:2163.
• Pagel KA et al. 2020. Integrated informatics analysis of cancer-related variants. JCO Clin Cancer Inform 4:310. (OpenCRAVAT)
• Kopanos C et al. 2019. VarSome: the human genomic variant search engine. Bioinformatics 35:1978.
• Stawiński P, Płoski R. 2024. Genebe.net: implementation and validation of an automatic ACMG variant pathogenicity criteria assignment. Clin Genet 106:119.
• myvariant.info docs: https://docs.myvariant.info/en/latest/
• BioThings field metadata: https://myvariant.info/v1/metadata/fields

Related Skills

• clinical-databases/clinvar-lookup - Source-level deep ClinVar queries
• clinical-databases/gnomad-frequencies - Source-level deep gnomAD queries
• clinical-databases/dbsnp-queries - Source-level rsID resolution
• clinical-databases/acmg-classification - ACMG framework with Pejaver calibration
• clinical-databases/variant-prioritization - Pipeline using aggregated annotations