Annotating variants & normalizing HGVS

Turn raw genomic variants — VCF rows, rsIDs, or HGVS strings — into annotated, consequence-predicted records, and link them to the clinical context OpenMed extracts from text (genes, variants, oncology findings, phenotype). The workhorse for a quick, no-install annotation is the Ensembl VEP REST API; for scale, run VEP, SnpEff, or ANNOVAR offline.

These annotators are free and license-permissive. Restricted clinical interpretation databases (e.g. licensed HGMD) are user-supplied — this skill sticks to open resources (Ensembl, gnomAD, ClinVar).

When to use

• You have a VCF / HGVS / rsID and need consequence predictions (missense, stop-gain, splice), affected transcripts, and protein change.
• You need to normalize HGVS to genomic coordinates (and back) on a known build (GRCh38 by default; GRCh37 via the dedicated endpoint).
• You want gnomAD population allele frequencies to flag common vs rare.
• You are pairing molecular findings with the phenotype/oncology context that OpenMed pulls from notes or literature.

Quick start (real Ensembl VEP REST call)

Base URL: https://rest.ensembl.org (GRCh38). For GRCh37 use https://grch37.rest.ensembl.org. Default species is human/homo_sapiens.

import requests

REST = "https://rest.ensembl.org"
HEADERS = {"Content-Type": "application/json", "Accept": "application/json"}

def vep_hgvs(hgvs: str) -> list[dict]:
    """Annotate a single HGVS variant (GET)."""
    r = requests.get(f"{REST}/vep/human/hgvs/{hgvs}", headers=HEADERS, timeout=30)
    r.raise_for_status()
    return r.json()

# Transcript-level HGVS (coding) — note the build-aware default transcript set
ann = vep_hgvs("ENST00000269305.9:c.215C>G")   # TP53 example
v = ann[0]
print(v["most_severe_consequence"])            # e.g. "missense_variant"
for tc in v.get("transcript_consequences", []):
    print(tc["gene_symbol"], tc.get("hgvsp"), tc.get("sift_prediction"),
          tc.get("polyphen_prediction"))

Batch many variants with the POST endpoint (region "CHROM POS ID REF ALT . . ." format, up to 200 per request):

def vep_region_batch(variants: list[str]) -> list[dict]:
    body = {"variants": variants}   # ["17 7676154 . C G . . .", ...] 1-based
    r = requests.post(f"{REST}/vep/human/region", headers=HEADERS,
                      json=body, timeout=60)
    r.raise_for_status()
    return r.json()

Equivalent cURL:

curl 'https://rest.ensembl.org/vep/human/hgvs/ENST00000269305.9:c.215C>G' \
  -H 'Content-Type:application/json'

Response highlights per variant: most_severe_consequence, transcript_consequences[] (gene_symbol, hgvsc, hgvsp, sift_prediction, polyphen_prediction, impact), and colocated_variants[] (rsIDs and population frequencies). Request gnomAD frequencies and ClinVar via VEP options / plugins.

Population frequencies via gnomAD (GraphQL)

For authoritative allele frequencies, query the gnomAD GraphQL API at https://gnomad.broadinstitute.org/api. Use variant IDs in chrom-pos-ref-alt form. Frequencies are derived from ac/an (allele count / number) — request those, not a non-existent af on subpopulations.

GNOMAD = "https://gnomad.broadinstitute.org/api"

QUERY = """
query Variant($id: String!, $ds: DatasetId!) {
  variant(variantId: $id, dataset: $ds) {
    variant_id rsids
    genome { ac an af homozygote_count }
    exome  { ac an af homozygote_count }
  }
}"""

def gnomad_freq(variant_id: str, dataset: str = "gnomad_r4") -> dict:
    r = requests.post(GNOMAD, json={"query": QUERY,
        "variables": {"id": variant_id, "ds": dataset}}, timeout=30)
    r.raise_for_status()
    return r.json()["data"]["variant"]

# gnomad_freq("17-7676154-C-G")  -> ac/an/af for exome and genome

Offline annotation at scale

For whole-VCF jobs, run a local annotator instead of per-variant REST calls:

All emit per-variant gene, consequence, and (with the right database) frequency and clinical fields. Keep the reference build (GRCh38) consistent end to end.

Workflow

1. Normalize input to a canonical form: left-align/trim VCF alleles; for HGVS, confirm the reference transcript and build.
2. Annotate — REST (/vep/human/hgvs or /vep/human/region) for a handful, offline VEP/SnpEff for a VCF.
3. Attach frequencies from gnomAD; flag common variants (e.g. AF > 1%).
4. Filter/prioritize by most_severe_consequence, impact, and rarity.
5. Join to clinical context from OpenMed (gene/variant mentions, oncology, phenotype) to assemble an interpretable record.

Hand-off to / from OpenMed

• OpenMed → variant context. openmed.analyze_text(report, model_name=<a Genomics or Oncology model>) extracts gene symbols, variant mentions (e.g. "EGFR L858R"), and tumor/oncology findings from pathology or molecular reports. Use those to (a) select which VCF variants matter and (b) attach phenotype context to each annotation.
• Variant → OpenMed. Free-text variant descriptions in reports can be normalized to HGVS here, then the surrounding clinical narrative is structured by OpenMed — linking genotype to extracted phenotype/diagnosis.
• Keep genomic + clinical data local. The REST/GraphQL calls carry only the variant coordinates (public allele data), never patient identifiers — and any narrative is de-identified with openmed.deidentify first.

Edge cases & gotchas

• Build mismatch is the #1 error. GRCh38 coordinates against a GRCh37 endpoint (or cache) give wrong genes. Use grch37.rest.ensembl.org only for GRCh37 data; default REST is GRCh38.
• Transcript choice changes the HGVS. c./p. notation depends on the reference transcript (MANE Select vs others). Pin the transcript explicitly.
• Normalize before annotating. Un-left-aligned indels and multi-allelic VCF rows produce inconsistent annotations — decompose and normalize first (e.g. bcftools norm).
• REST is rate-limited. ~15 req/s and 200 variants/POST on the Ensembl REST server; switch to offline VEP for large VCFs. Honor Retry-After on 429.
• gnomAD subpopulation fields. Query ac/an (and compute AF) for subpopulations; some schema paths reject af directly — track the current schema version, which changes between gnomAD releases.
• No clinical interpretation here. Consequence ≠ pathogenicity. Pathogenicity classification (ACMG/AMP) uses curated evidence and licensed databases the user supplies; this skill produces annotations, not diagnoses.

Standards & references

• VCF specification — https://samtools.github.io/hts-specs/VCFv4.4.pdf
• HGVS nomenclature — https://hgvs-nomenclature.org/
• Ensembl VEP REST (HGVS GET) — https://rest.ensembl.org/documentation/info/vep_hgvs_get
• Ensembl VEP REST (region POST) — https://rest.ensembl.org/documentation/info/vep_region_post
• Ensembl VEP (offline) — https://www.ensembl.org/info/docs/tools/vep/index.html
• SnpEff — https://pcingola.github.io/SnpEff/
• gnomAD API — https://gnomad.broadinstitute.org/api
• ClinVar — https://www.ncbi.nlm.nih.gov/clinvar/