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Opentargets Database

Google DeepMindOfficialPublished by Google DeepMind

  • Skill
  • Tool integration
  • Science and research
  • Apache-2.0
  • Low risk
  • Has scripts
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From the author

Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification.
— the description by Google DeepMind

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Written by AutoGineer from the skill’s own files, not by the author

The Open Targets Database skill provides a structured interface to the Open Targets Platform GraphQL API. It enables researchers to retrieve aggregated evidence from genetics, pathways, animal models, and clinical trials. By using the provided Python wrapper, users can perform complex queries to rank target-disease associations, identify druggable genes, and explore clinical trial data while adhering to API best practices and fair-use policies.

What it does

  • Fetches target-disease associations and ranks them based on multi-modal evidence.
  • Retrieves druggability, tractability, and clinical safety data for specific gene targets.
  • Identifies drugs and clinical candidates associated with specific diseases.
  • Performs Locus-to-Gene (L2G) predictions to identify causal genes at genomic loci.
  • Searches for disease metadata using standard identifiers like MONDO or EFO.
  • Executes custom GraphQL queries for advanced data retrieval beyond built-in commands.

When to use it

  • When identifying potential therapeutic targets for a specific disease.
  • When exploring the druggability of a gene or protein.
  • When investigating the genetic evidence supporting a target-disease association.
  • When looking for clinical trial information for drugs related to a specific condition.
  • When performing fine-mapping analysis using credible sets from GWAS studies.

Advantages

  • Simplifies complex GraphQL API interactions into easy-to-use command-line subcommands.
  • Includes built-in retry logic and fair-use rate limiting.
  • Automatically handles response truncation to manage context window usage.
  • Provides standardized tools for genomic range searches and L2G analysis.
  • Ensures consistent output formatting via JSON files.

Usage examples

“Find all target genes associated with Rheumatoid Arthritis.”
The agent uses the get-associated-targets command with the appropriate disease ID to return a list of associated genes and their evidence scores.
“What is the druggability status of the gene ENSG00000169083?”
The agent executes the get-target-druggability command to retrieve tractability data and clinical trial safety information for the specified Ensembl ID.
“Find all drugs in Phase III or higher for a specific disease.”
The agent uses the get-disease-drugs command with the --min-stage PHASE_3 flag to filter and return relevant clinical candidates.
“Identify the most likely causal gene for a specific variant.”
The agent runs the get-l2g command for the given variant ID to retrieve Locus-to-Gene predictions.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/google-deepmind/opentargets-database/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip opentargets-database.zip -d .agents/skills

Documentation by Google DeepMind

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 disease_id: Fetches all GWAS studies associated with a specific disease ID (e.g. MONDO_0008383 for Rheumatoid Arthritis).

  • 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 disease_id: Find all target genes associated with a specific disease ID (EFO or MONDO).

  • get-disease-drugs disease_id [--min-stage STAGE]: Find all drugs and clinical candidates associated with a disease. Use --min-stage to filter (e.g., PHASE_3 for Phase III or Approved).

  • 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 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:

StarsConfidence String (API value)
★★★★ (4 stars)SuSiE fine-mapped credible set with in-sample LD
★★★ (3 stars)SuSiE fine-mapped credible set with out-of-sample LD
★★ (2 stars)`PICS fine-mapped credible set extracted from summary
: : statistics` :
★ (1 star)PICS fine-mapped credible set based on reported top hit
NoneUnknown confidence

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 are typically MONDO IDs (e.g. MONDO_0008383 for Rheumatoid Arthritis) or EFO IDs (e.g. EFO_0009460). Use the search-disease command to find the correct ID.

    • 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.

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

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Attribution and licence

Published by
Google DeepMind
Publisher
Google DeepMind (Official)
Source
google-deepmind/science-skills · skills/opentargets_database · commit 4de6786
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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