clinvar-database

ClinVar Database

Prerequisites

1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.
2. User Notification: If .licenses/clinvar_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://www.ncbi.nlm.nih.gov/clinvar/, then (2) create the file recording the notification text and timestamp.
3. .env file: Make sure the .env file exists in your home directory. Create one if it does not exist.
4. NCBI_API_KEY (optional): Raises the NCBI rate limit from 3 to 10 requests/second. The skill works without it, but a key is recommended if the user plans many queries or encounters a 429 error. You can register for a key for free at https://www.ncbi.nlm.nih.gov/account/settings/. You MUST use the safe credentials protocol in the credentials skill to check for and request this key if this skill looks relevant to the user's request.

Overview

ClinVar is the primary consensus record for clinical classifications of human genomic variations. It provides the "clinical ground truth" for pathogenicity labels (Pathogenic, Likely Pathogenic, Benign, VUS) based on assertions from global laboratories.

When to Use

Use when you need to:

• Find the current clinical significance and star rating (review status) for a specific variant.
• Fetch clinician notes, assertion criteria, or rationales for previous clinical laboratory classifications.
• Retrieve the preferred condition name and associated HPO terms for a specific variant.
• Find a list of variant controls (e.g., "Find all Pathogenic variants in the HBB gene within 50bp of a signal").
• Check for conflicting interpretations for a given variant and identify the organizations submitting each classification.

Do NOT use when you need to:

• Find specific allele frequencies in global populations (use gnomAD).
• Describe the normal biological role of a protein and typical inheritance patterns (use OMIM).
• Predict mechanistic effects of novel mutations, like frameshifts or exon skipping (use AlphaGenome).
• Find recommended surveillance schedules for patients with a pathogenic variant (use GeneReviews).
• Generate or view 3D structural models of affected proteins (use PDB / AlphaFold).

Quick Start

ClinVar queries are executed via a robust Python wrapper script to handle strict rate limiting and XML/JSON parsing.

Example: Search for BRCA1 variants

uv run scripts/clinvar_api.py search --query "BRCA1[gene]" --output results.json

Core Rules

• Retmax Constraint: The search command defaults to --retmax 200. For any "List all" or gene-wide request, you MUST explicitly set --retmax higher (e.g., 1000) to ensure data completeness.
• Use the Wrapper: Prefer the wrapper script for standard queries. It handles rate limiting, retries, and the complex XML parsing for you. If the script's parsed output does not contain the specific fields you need, you may modify the script or query the NCBI E-utilities API directly — but be aware that the raw XML schemas are complex and vary between record types.
• If the rate limit is hit, the script will throw a clear error. You MUST use the safe credentials protocol in the credentials skill to check for and request the NCBI_API_KEY to help the user add it to their .env file.
• Notification: If this skill is used, ensure this is mentioned in the output.

Utility Scripts

1. count — Count Matching Variants

Purpose: Check how many variants match a query without fetching IDs. Use to decide whether a full search is warranted.

Arguments:

• --query: (Required) NCBI Entrez search query string.
• --output: (Required) Output JSON file path.

Example: uv run scripts/clinvar_api.py count \ --query "TP53[gene] AND \"uncertain significance\"[clinsig]" \ --output count.json Output: {"total_count": <int>}

2. search — Search Variants

Purpose: Identify variants based on genomic location, gene symbols, or clinical attributes using NCBI Entrez search syntax. The search command automatically paginates through all matching results to ensure complete, deterministic retrieval.

# Fetch ALL matching variants (default behavior)
uv run scripts/clinvar_api.py search \
  --query "BRCA1[gene]" --output results.json

# Search by Chromosome and Position Range
uv run scripts/clinvar_api.py search \
  --query "11[chr] AND 5225000:5226000[chrpos]" --output results.json

# Combine terms using Entrez syntax
uv run scripts/clinvar_api.py search \
  --query "HBB[gene] AND pathogenic[clinsig]" --output results.json

# Cap results at 50
uv run scripts/clinvar_api.py search \
  --query "TP53[gene]" --retmax 50 --output results.json

Arguments:

• --query: (Required) NCBI Entrez search query string.
• --retmax: Maximum total number of variant IDs to return. Default is 0, which means "fetch all matching results." Set to a positive integer to cap the result set.
• --page_size: Number of IDs to fetch per API request (default: 500, max: 10000 per NCBI limits).
• --output: (Required) Output JSON file path.

Output: A JSON object containing:

• total_count — Total number of matching variants in ClinVar.
• fetched_count — Number of IDs actually retrieved.
• variant_ids — List of ClinVar Variation ID strings.

3. summary — Get Interpretation Summary

Purpose: Retrieve top-line clinical significance labels, star ratings (review status), and basic phenotype data for rapid variant screening.

# Get summary for one or more Variation IDs
uv run scripts/clinvar_api.py summary \
  --variant_ids 12345 67890 --output summary.json

Arguments:

• --variant_ids: (Required) One or more ClinVar Variation IDs.
• --output: (Required) Output JSON file path.

Output: A JSON list of summary objects, each containing:

• variant_id, title, clinical_significance, review_status,
  last_evaluated, phenotypes
• genes — list of {gene_id, symbol, strand}
• variation_type — e.g., single nucleotide variant, Deletion, Insertion
• molecular_consequences — list of strings (e.g., ["missense variant",
  "nonsense"])

4. evidence — Get Clinical Evidence

Purpose: Fetch the full clinical record for a single variant, including free-text clinician rationales, assertion methods, and specific submitter notes.

# Get full evidence for a single Variation ID
uv run scripts/clinvar_api.py evidence \
  --variant_id 12345 --output evidence.json

Arguments:

• --variant_id: (Required) A single ClinVar Variation ID.
• --output: (Required) Output JSON file path.

Output: A JSON object containing:

• variant_id
• allele_info — {chromosome, position_start, position_stop, reference_allele, alternate_allele, cytogenetic_band, dbsnp_rsid} (GRCh38 preferred)
• conditions — list of {name, medgen_cui, omim_id, orphanet_id, hpo_terms}
• functional_consequences — list of {value, sequence_ontology_id}
• structural_variant_details — {outer_start, inner_start, inner_stop, outer_stop, copy_number} (present only for CNVs, otherwise null)
• citation_references — list of PubMed IDs cited in the global "Citations" section
• submissions — list of per-submitter records, each containing:
  • submitter_name, classification, curator_notes, assertion_criteria
  • date_last_evaluated — when the submitter last reviewed the classification

Typical Workflows

Count-First Workflow (Recommended)

For large or unknown result sets, use count first to decide whether to proceed, then search (which auto-paginates and returns total_count / fetched_count), then summary to screen.

# Step 1: Gauge size (optional — search also returns total_count)
uv run scripts/clinvar_api.py count \
  --query "HBB[gene] AND pathogenic[clinsig]" --output count.json

# Step 2: Fetch all variant IDs (auto-paginates)
uv run scripts/clinvar_api.py search \
  --query "HBB[gene] AND pathogenic[clinsig]" --output ids.json

# Step 3: Get summaries (extract variant_ids from search output)
uv run scripts/clinvar_api.py summary \
  --variant_ids 12345 67890 --output summary.json

Deep Dive: search → evidence

When you need the full clinical picture for a specific variant — including submitter rationales, PubMed citations, ontology-linked conditions, and allele coordinates — use evidence.

uv run scripts/clinvar_api.py evidence \
  --variant_id 12345 --output evidence.json

Workflow: Robust Variant Discovery (Triangulation)

ClinVar metadata is inconsistent. To fulfill "List all" requests, do not rely on a single filter. Perform the following in a single turn and merge results:

1. Search by exact label (e.g., "3 prime UTR variant"[molecular_consequence]).
2. Search by HGVS nomenclature pattern (e.g., c.*).
3. Search by genomic coordinate range (using [chrpos]).

This "triangulation" ensures structural variants with missing labels are not overlooked.

Verifying Coding vs. Non-Coding Status via HGVS

molecular_consequences alone can be ambiguous (e.g., splice donor variant appears in both coding and non-coding contexts). Always cross-check the title field for HGVS patterns:

• c.-… — 5' UTR (non-coding)
• c.*… — 3' UTR (non-coding)
• c.123+N / c.123-N — intronic (non-coding)
• p.Trp146Arg etc. — protein effect (coding)

A variant with UTR/intronic HGVS and no p. annotation is non-coding, even with splicing labels. Conversely, any p. annotation indicates a coding effect.

ClinVar Metadata Reference

• 3' UTR
  • Search String: "3 prime UTR variant"[mol_consequence]
  • HGVS: c.*
• 5' UTR
  • Search String: "5 prime UTR variant"[mol_consequence]
  • HGVS: c.-
• To find "high-confidence" variants or expert-reviewed consensus, use the review_status filter. This is the most efficient way to distinguish between single-laboratory assertions and panel-reviewed ground truth.

When to Use Which Fields

• Quick pathogenicity label — Use summary → clinical_significance
• Gene symbol and strand — Use summary → genes
• Variant type (SNV, del, etc.) — Use summary → variation_type
• Protein-level effect — Use summary → molecular_consequences
• Genomic coordinates (GRCh38) — Use evidence → allele_info
• Linked conditions (ontology) — Use evidence → conditions
• SO functional consequence — Use evidence → functional_consequences
• CNV breakpoints/copy number — Use evidence → structural_variant_details
• PubMed references — Use evidence → citation_references
• Date of last lab review — Use both → last_evaluated
• Clinician rationales — Use evidence → submissions[].curator_notes

Retrieving Genomic Coordinates (Default HG38/GRCh38)

To get precise genomic coordinates in the format <chrom>:<pos>:<ref>><alt> (e.g., chr5:70951945:G>A), you must use the evidence command, as these details are not available in the summary output.

You MUST always include genomic coordinates in the format <chrom>:<pos>:<ref>><alt> when listing or presenting variants, even if not explicitly requested by the user. If coordinates are missing from the summary, use the evidence command or dbSNP fallback to retrieve them.

1. Fetch Evidence: Use uv run scripts/clinvar_api.py evidence --variant_id <ID> --output evidence.json.
2. Extract VCF Attributes: The evidence command parses the XML. Extract:
   • Chromosome: Chr
   • Position: positionVCF (or start)
   • Ref: referenceAlleleVCF (or referenceAllele)
   • Alt: alternateAlleleVCF (or alternateAllele) from the SequenceLocation element with Assembly="GRCh38".

Fallback for Imprecise Coordinates (Gene Range): ClinVar often returns the full gene range for non-coding variants. If the extracted coordinates correspond to the gene range instead of a specific position, use the dbsnp-database skill to resolve the precise coordinates using the dbsnp_rsid or HGVS title: 1.Check for dbsnp_rsid in the evidence output. 2. Run uv run scripts/dbsnp_cli.py resolve-rsid {rsid} to get precise GRCh38 coordinates. 3. Format as <chrom>:<pos>:<ref>><alt> using the SPDI or HGVS data from dbSNP.

Structural Variant Note

The structural_variant_details field is only populated for copy number variants (CNVs). For standard SNVs and small indels this field will be null. Use the allele_info fields (position_start, position_stop, reference_allele, alternate_allele) instead.

CNV / Large Deletion Note

Large copy-number variants (CNVs) frequently have empty molecular_consequences. If a variant title mentions "del" and coordinates overlap your target region, it is relevant regardless of missing labels.

Obtaining and Using an API Key

You can register for a key for free at https://www.ncbi.nlm.nih.gov/account/settings/. You MUST use the safe credentials protocol in the credentials skill to check for and request this key if this skill looks relevant to the user's request.

Best Practices

• Always use uv run to execute python.
• If jq is unavailable pivot immediately to using Python one-liners for processing JSON (e.g., uv run python3 -c "import json; ...").
• Use count before search to understand the result set size.
• The search command fetches all results by default and includes total_count and fetched_count in the output — always verify these match to confirm complete retrieval.
• Entrez results are unsorted. To order by date, fetch all results and sort locally by last_evaluated.

Common Mistakes

• Attempting to parse the E-utilities XML yourself — Always use the provided clinvar_api.py client which handles the unpredictable XML schemas robustly.
• Getting HTTP 429 Too Many Requests — The client throws an exception telling you to pause. You MUST use the safe credentials protocol in the credentials skill to check for and request the NCBI_API_KEY to help the user add it to their .env file, then retry.
• Sending raw DNA sequences to the API — The API expects HGVS nomenclature, RS IDs, or proper Entrez coordinate syntax (11[chr] AND 1234[chrpos]), not raw ATCG strings.
• For synonymous or non-coding variants — HGVS nomenclature (e.g., CAPN3 AND "c.551C>T") is more reliable than coordinate searches ([chrpos]), as many ClinVar records for these types lack precise genomic mappings.
• Case sensitivity in molecular consequences — ClinVar returns mixed-case strings. Always use case-insensitive matching (.lower()) when filtering.
• Parsing search output as a bare list — search returns a JSON object with total_count, fetched_count, and variant_ids — not a bare list.