Opentargets Database

Open Targets Database Skill

Overview

This skill provides access to the Open Targets Platform GraphQL API. It aggregates multi-modal evidence from genetics (GWAS/eQTL), pathways, animal models, and clinical trials to rank target-disease associations and identify druggable genes.

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/opentargets_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://platform-docs.opentargets.org/licence, then (2) create the file recording the notification text and timestamp.

Core Rules

• Use the Wrapper: ALWAYS execute the provided helper scripts to query the database rather than accessing the database directly. The scripts automatically enforce fair use and implement retry logic.
• Output Flag: The --output flag is always required as output can be very large. Use jq or write your own code to process this JSON file.
• Notification: If this skill is used, ensure this is mentioned in the output.

Quick Reference

Always use the provided Python script scripts/query_opentargets.py to quickly query the database. It handles API communication, retries, formatting, and automatically truncates overly large responses. NEVER write your own curl or similar requests.

Usage:

uv run scripts/query_opentargets.py --output /tmp/opentargets_results.json [OPTIONS] COMMAND [ARGS]...

Common Options:

• --output PATH: Required. Path to write the JSON output file.
• --limit N: Limit the number of items returned in arrays (default is 50). Use a smaller number like 10 when doing preliminary exploration.
• --page-size N: Set the API pagination size (default is 200). Increase if you need more results (e.g., a study with many credible sets).

Available Commands:

• get-gwas-studies efo_id: Fetches all GWAS studies associated with a specific disease ontology EFO ID (e.g. EFO_0000685).
• get-study-credible-sets study_id: Fetches all credible sets for a given study ID (e.g. FINNGEN_R12_RX_CROHN_2NDLINE). Returns confidence, finemapping method, variant, and p-value info.
• get-qtl-credible-sets variant_id: Retrieves QTL credible sets for a specific variant ID (e.g. 19_44908822_C_T).
• get-l2g variant_id [--study-id ID]: Returns Locus-to-Gene (L2G) predictions/scores for a locus to identify the most likely causal gene. Only variant_id is required; use --study-id to filter to a specific study. Accepts chr prefix (e.g. chr1_113834946_A_G).
• get-target-druggability ensembl_id: Provides tractability data (small molecule, antibody, etc.) and clinical trial safety info for a gene/target.
• get-associated-targets efo_id: Find all target genes associated with a specific disease EFO ID.
• get-associated-diseases ensembl_id: Find all diseases associated with a specific target Ensembl ID.
• search-disease query_string: Search for a disease by name to find its EFO ID and other metadata.
• get-credible-sets-near-target ensembl_id [--window N]: Fetches credible sets for a target and filters them to those within a genomic window around the target. Useful for finding variants "nearby" a gene.
• custom-query query [--variables '{}']: Run a raw GraphQL query for any other Open Targets data.

L2G Query Usage

The get-l2g command has two modes:

• Variant only (get-l2g <variant_id>): Returns L2G predictions from all credible sets across all studies where that variant is the lead variant. This can return a large number of results (e.g., hundreds). Use this when the user wants a broad view of which gene is most likely causal at a locus, or when no specific study is mentioned.
• Variant + study (get-l2g <variant_id> --study-id <study_id>): Returns L2G predictions only for credible sets from that specific study. Use this when the user asks about a specific GWAS study or when you need to narrow down the results.

Incomplete results warning: The variant-only mode can return hundreds of credible sets. The default --page-size is 200, so if the API reports a count higher than the number of rows returned, you are seeing incomplete results. Always compare count to the actual number of rows. If they differ, either increase --page-size or inform the user that only a subset was retrieved.

Querying by Region

To find studies with variants "nearby" a gene, use get-credible-sets-near-target, which improves upon the base API by performing a flexible search based on genomic position: uv run scripts/query_opentargets.py --output /tmp/results.json get-credible-sets-near-target ENSG00000156515 --window 500000

Note that the Open Targets GraphQL schema includes a regions parameter for credibleSets, however it performs an exact match against pre-computed region strings (e.g., chr10:68769984-69903496) and there is some missing data. Use get-credible-sets-near-target as it allows a genomic range overlap search.

This fetches credible sets associated with the target and filters them in Python based on the variant's genomic position.

Advanced GraphQL Queries

If you need to query endpoints or fields not exposed by the built-in subcommands, use the custom-query subcommand.

Before writing a custom query: Read the reference documentation to understand the API schema, types, and see example queries. See references/OpenTargets_GraphQL_Guide.md for full schema details, endpoints, and examples.

Example: Finding drugs for a disease

uv run scripts/query_opentargets.py custom-query \
  query drugsForDisease($id: String!) {
    disease(efoId: $id) {
      name
      drugAndClinicalCandidates {
        count
        rows {
          maxClinicalStage
          drug {
            id
            name
          }
        }
      }
    }
  }' \
--variables '{"id": "EFO_1001006"}'
--output '/tmp/opentargets_result.json'

Confidence Star Ratings

The Open Targets Platform assigns a confidence level to each credible set based on the fine-mapping method and quality checks. These correspond to star ratings displayed in the platform UI:

When users ask about "N-star confidence", match their request to the corresponding string in the confidence field of the API response.

Tips and Common Mistakes

• ID Formats:
  • Disease IDs must be in EFO format (e.g. EFO_0000685).
  • Target IDs must be Ensembl IDs (e.g. ENSG00000169083), not HGNC symbols. If you only have a gene symbol, you may need to map it first using a custom GraphQL search query.
  • Variant IDs are formatted as chromosome_position_ref_alt (e.g., 1_154426264_C_T). A chr prefix (e.g. chr1_154426264_C_T) is automatically stripped by the tool.
  • Study IDs can be GWAS Catalog IDs (e.g. GCST90204201) or project-specific IDs (e.g. FINNGEN_R12_RX_CROHN_2NDLINE).
• Truncation: The tool truncates arrays longer than --limit to protect the context window. If you see "_truncated", you can run the query again with a higher limit if you specifically need more data, but be cautious with large limit values. Always use the --output flag to save the result to a file and avoid terminal output truncation.
• Pagination and incomplete results: The --page-size option (default:
  200. controls how many items are fetched from the API. Always check the count field in the response and compare it to the number of rows actually returned. If count > number of rows, you have incomplete data — either increase --page-size to fetch more, or inform the user that only a partial result set was returned. This is especially important for get-l2g without --study-id, which can return hundreds of credible sets.