Generated banner for Quickgo Database
Generated logo for Quickgo Database

Quickgo Database

Google DeepMindOfficialPublished by Google DeepMind

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

From the author

Query the QuickGO and Evidence & Conclusion Ontology (ECO) REST API. Use this when you need to map genes to biological processes, molecular functions, or cellular components, find genes associated with a specific pathway/GO term, or explore the Gene Ontology hierarchy. Do not use for querying drug targets (use OpenTargets) or mechanistic signaling pathway diagrams (use KEGG).
— the description by Google DeepMind

AutoGineer summary

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

The QuickGO Database skill provides a command-line interface to interact with the QuickGO and Evidence & Conclusion Ontology (ECO) REST API. It enables researchers to map gene products to their biological processes, molecular functions, and cellular components. The tool facilitates exploring the Gene Ontology hierarchy, resolving gene symbols to database identifiers, and retrieving evidence codes to validate functional annotations. It is designed to handle API rate limits and pagination, ensuring reliable data retrieval for bioinformatics workflows.

What it does

  • Maps gene symbols to formal database identifiers like UniProtKB IDs.
  • Retrieves functional annotations linking genes to biological processes, molecular functions, and cellular components.
  • Explores the Gene Ontology hierarchy including ancestors, descendants, and term definitions.
  • Searches and retrieves details for Evidence & Conclusion Ontology (ECO) terms.
  • Supports filtering by taxonomy, evidence codes, and GO aspects.
  • Generates GO Slim summaries for high-level functional analysis of gene lists.

When to use it

  • When you need to identify the biological functions or cellular locations of specific genes.
  • When you are performing functional enrichment analysis or mapping gene lists to GO terms.
  • When you need to verify the evidence type (e.g., experimental vs. electronic) for a gene annotation.
  • When you need to explore the relationship between different Gene Ontology terms.

Advantages

  • Provides a standardized, automated way to query EMBL-EBI's QuickGO resources.
  • Handles API rate limiting and pagination automatically.
  • Allows for precise filtering by experimental evidence codes to reduce noise.
  • Supports structured output to JSON for downstream data processing.

Usage examples

“Find all experimentally validated biological process annotations for the human gene PROC.”
The agent resolves the gene symbol to a UniProtKB ID and queries the annotation endpoint with the experimental evidence code filter, saving the results to a JSON file.
“Get the definition and ancestors for the GO term GO:0003150.”
The agent uses the go terms subcommand to fetch the term's metadata and its hierarchical lineage, outputting the data to a file.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/google-deepmind/quickgo-database/download
  2. Unzip it into the cross-client skills folder of your project:

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

Documentation by Google DeepMind

QuickGO Database Skill

GO (Gene Ontology) annotations are one of the main ways to label a gene's function. QuickGO is a fast, web-based browser for the GO and Evidence & Conclusion Ontology (ECO), maintained by the Gene Ontology Annotation (GOA) group at EMBL-EBI.

It provides a centralised resource to explore the functional attributes of gene products (proteins, RNA, and complexes). It is a primary tool for functional annotation mapping since it allows you to link a gene (e.g., USH2A) to its specific biological processes (e.g. sensory perception of light stimulus), molecular functions, and cellular components.

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/quickgo_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.ebi.ac.uk/QuickGO/ and https://www.ebi.ac.uk/QuickGO/api/index.html, then (2) create the file recording the notification text and timestamp.

Usage

This skill provides a Python CLI wrapper scripts/quickgo_tool.py that queries the QuickGO REST API. It handles formatting the requests, respecting rate limits, and safely storing the potentially large JSON responses.

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 the required rate limit gracefully.

  • Pagination & Limits: Restrict endpoints to a maximum of 100 results per page using --limit 100 and the --page parameter for larger datasets.

  • Output Files: Always use the --output flag to save responses to a file incrementally or parse via jq.

  • Evidence Codes: Prioritize experimental evidence (ECO:0000269) over electronic (ECO:0000501) to avoid noisy predictions.

  • Taxon Filtering: Use --taxonId 9606 to restrict results to Human when analysing clinical or human genomic data.

  • Notification: If this skill is used, ensure this is mentioned in the output.

The tool has four main subcommands:

  1. go: For retrieving information about GO terms (e.g. definitions, ancestors, descendants, and slims). See references/go_terms.md.

  2. annotation: For finding functional annotations linking gene products to GO terms. This is your primary functional mapper. See references/annotations.md.

  3. geneproduct: For resolving gene symbols (like PROC) to their formal database identifiers. See references/gene_products.md.

  4. eco: For Evidence & Conclusion Ontology terms (used in annotations to indicate how an annotation was derived, e.g. experimental vs electronic). See references/eco_terms.md.

Common Workflows

1. Map a gene to its functions (Annotations)

To find out what a gene does, you must first resolve its symbol to a UniProtKB ID, and then query its annotations. Often it is best to filter for experimental evidence (e.g. ECO:0000269 for EXP, or others like IDA, IMP) to avoid noisy electronic predictions.

# Step 1: Find the UniProtKB ID for human (9606) gene PROC
uv run scripts/quickgo_tool.py geneproduct search --query "PROC" --taxonId 9606 --limit 5 --output proc_id.json
# (Look at proc_id.json, observe the ID is e.g., UniProtKB:P04070)

# Step 2: Find experimental GO annotations for that ID
uv run scripts/quickgo_tool.py annotation search --geneProductId "UniProtKB:P04070" --taxonId 9606 --evidenceCode "ECO:0000269" --limit 50 --output proc_annotations.json
2. Find all genes in a pathway

To find all genes annotated to a specific GO term (e.g., GO:0003700 for "transcription factor activity"):

# Find human genes with this specific molecular function
uv run scripts/quickgo_tool.py annotation search --goId "GO:0003700" --taxonId 9606 --limit 50 --output tf_genes.json
3. Explore the GO Hierarchy

To check if a specific GO term is a descendant of a broader category, or to fetch its definition:

# Fetch term details (definitions, synonyms)
uv run scripts/quickgo_tool.py go terms --ids "GO:0003150" --output term_details.json

# Check ancestry (e.g., is GO:0001917 a child of something?)
uv run scripts/quickgo_tool.py go terms --ids "GO:0001917" --relation ancestors --output term_ancestors.json
4. Create a GO Slim Summary

If you have a list of candidate genes and want a high-level functional summary, you can map them up to a predefined GO Slim. First, fetch the annotations for the genes to extract their GO IDs, then pass those IDs to the slim endpoint:

# Step 1: Find GO IDs for candidate genes (e.g., via their UniProt IDs, fetching their annotations)
# ... (output yields e.g., GO:0006915,GO:0008219)

# Step 2: Create a slim summary from those specific GO IDs
uv run scripts/quickgo_tool.py go slim --slimsToIds "GO:0005575,GO:0008150,GO:0003674" --slimsFromIds "GO:0006915,GO:0008219" --output my_slim.json

Files

scripts/quickgo_tool.py

# Copyright 2026 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""A command-line tool for querying the QuickGO API.

This script provides subcommands to interact with various QuickGO API endpoints,
including searching for GO terms, ECO terms, annotations, and gene products.
Results are saved to a specified JSON file.
"""

# /// script
# requires-python = ">=3.10"
# dependencies = [
#   "polite-http",
# ]
# ///

import argparse
import json
from typing import Any
import urllib.parse

from polite_http import http_client

BASE_URL = "https://www.ebi.ac.uk/QuickGO/services"
_CLIENT = http_client.HttpClient(BASE_URL, qps=10)


def make_request(path: str, params: dict[str, Any] | None = None) -> Any:
  """Makes a GET request to the QuickGO API.

  Args:
      path: The API endpoint path (e.g., "/ontology/go/search").
      params: Optional dictionary of query parameters.

  Returns:
      The JSON response from the API.
  """
  url = f"{BASE_URL}{path}"
  if params:
    url = f"{url}?{urllib.parse.urlencode(params, doseq=True)}"
  return _CLIENT.fetch_json(url)


def save_output(data: Any, filename: str):
  with open(filename, "w") as f:
    json.dump(data, f, indent=2)
  print(f"Successfully wrote results to {filename}")


def go_search(args: argparse.Namespace):
  limit = min(args.limit, 100) if args.limit else 25
  data = make_request(
      "/ontology/go/search",
      params={"query": args.query, "limit": limit, "page": args.page},
  )
  save_output(data, args.output)


def go_terms(args: argparse.Namespace):
  path = f"/ontology/go/terms/{args.ids}"
  if args.relation:
    path += f"/{args.relation}"
  if args.target_ids:
    path += f"/{args.target_ids}"
  data = make_request(path)
  save_output(data, args.output)


def go_slim(args: argparse.Namespace):
  params = {"slimsToIds": args.slimsToIds}
  if args.slimsFromIds:
    params["slimsFromIds"] = args.slimsFromIds
  if args.relations:
    params["relations"] = args.relations
  data = make_request("/ontology/go/slim", params=params)
  save_output(data, args.output)


def eco_search(args: argparse.Namespace):
  limit = min(args.limit, 100) if args.limit else 25
  data = make_request(
      "/ontology/eco/search",
      params={"query": args.query, "limit": limit, "page": args.page},
  )
  save_output(data, args.output)


def eco_terms(args: argparse.Namespace):
  path = f"/ontology/eco/terms/{args.ids}"
  if args.relation:
    path += f"/{args.relation}"
  data = make_request(path)
  save_output(data, args.output)


def annotation_search(args: argparse.Namespace):
  """Searches for annotations using the QuickGO API.

  Args:
      args: An argparse namespace containing the command-line arguments.
        Expected arguments include geneProductId, geneProductSubset,
        geneProductType, goId, taxonId, evidenceCode, goUsage, qualifier, limit,
        page, aspect, and output.
  """
  params = {}
  if args.geneProductId:
    params["geneProductId"] = args.geneProductId
  if args.geneProductSubset:
    params["geneProductSubset"] = args.geneProductSubset
  if args.geneProductType:
    params["geneProductType"] = args.geneProductType
  if args.goId:
    params["goId"] = args.goId
  if args.taxonId:
    params["taxonId"] = args.taxonId
  if args.evidenceCode:
    params["evidenceCode"] = args.evidenceCode
  if args.goUsage:
    params["goUsage"] = args.goUsage
  if args.qualifier:
    params["qualifier"] = args.qualifier
  if args.aspect:
    params["aspect"] = args.aspect
  if args.limit:
    params["limit"] = min(args.limit, 100)
  if args.page:
    params["page"] = args.page

  data = make_request("/annotation/search", params=params)
  save_output(data, args.output)


def geneproduct_search(args: argparse.Namespace):
  """Searches for gene products using the QuickGO API.

  Args:
      args: An argparse namespace containing the command-line arguments.
        Expected arguments include query, taxonId, limit, page, and output.
  """
  params = {}
  if args.query:
    params["query"] = args.query
  if args.taxonId:
    params["taxonId"] = args.taxonId
  if args.limit:
    params["limit"] = min(args.limit, 100)
  if args.page:
    params["page"] = args.page

  data = make_request("/geneproduct/search", params=params)
  save_output(data, args.output)


def main():
  parser = argparse.ArgumentParser(description="QuickGO API CLI Wrapper")
  subparsers = parser.add_subparsers(dest="command", required=True)

  # GO Subcommands
  go_parser = subparsers.add_parser("go", help="Gene Ontology endpoints")
  go_sub = go_parser.add_subparsers(dest="go_cmd", required=True)

  go_search_p = go_sub.add_parser("search", help="Search GO terms by text")
  go_search_p.add_argument(
      "--query", required=True, help="Text query (e.g. 'apoptosis')"
  )
  go_search_p.add_argument(
      "--limit", type=int, default=25, help="Max results per page (max 100)"
  )
  go_search_p.add_argument("--page", type=int, default=1, help="Page number")
  go_search_p.add_argument("--output", required=True, help="Output JSON file")
  go_search_p.set_defaults(func=go_search)

  go_terms_p = go_sub.add_parser("terms", help="Get GO term details")
  go_terms_p.add_argument("--ids", required=True, help="Comma-separated GO IDs")
  go_terms_p.add_argument(
      "--relation",
      choices=["ancestors", "descendants", "children", "complete", "paths"],
      help="Relationship type",
  )
  go_terms_p.add_argument(
      "--target_ids", help="Target IDs (only used with 'paths' relation)"
  )
  go_terms_p.add_argument("--output", required=True, help="Output JSON file")
  go_terms_p.set_defaults(func=go_terms)

  go_slim_p = go_sub.add_parser("slim", help="Calculate GO Slims")
  go_slim_p.add_argument("--slimsToIds", required=True, help="Target slim IDs")
  go_slim_p.add_argument("--slimsFromIds", help="Source IDs")
  go_slim_p.add_argument("--relations", help="Comma-separated relations")
  go_slim_p.add_argument("--output", required=True, help="Output JSON file")
  go_slim_p.set_defaults(func=go_slim)

  # ECO Subcommands
  eco_parser = subparsers.add_parser(
      "eco", help="Evidence & Conclusion Ontology"
  )
  eco_sub = eco_parser.add_subparsers(dest="eco_cmd", required=True)

  eco_search_p = eco_sub.add_parser("search", help="Search ECO terms by text")
  eco_search_p.add_argument("--query", required=True)
  eco_search_p.add_argument(
      "--limit", type=int, default=25, help="Max results per page (max 100)"
  )
  eco_search_p.add_argument("--page", type=int, default=1, help="Page number")
  eco_search_p.add_argument("--output", required=True)
  eco_search_p.set_defaults(func=eco_search)

  eco_terms_p = eco_sub.add_parser("terms", help="Get ECO term details")
  eco_terms_p.add_argument("--ids", required=True)
  eco_terms_p.add_argument(
      "--relation",
      choices=["ancestors", "descendants", "children", "complete", "paths"],
  )
  eco_terms_p.add_argument("--output", required=True)
  eco_terms_p.set_defaults(func=eco_terms)

  # Annotation Subcommands
  ann_parser = subparsers.add_parser(
      "annotation", help="Annotation search endpoints"
  )
  ann_sub = ann_parser.add_subparsers(dest="ann_cmd", required=True)

  ann_search_p = ann_sub.add_parser("search", help="Search annotations")
  ann_search_p.add_argument(
      "--geneProductId", help="Gene product ID (e.g. UniProtKB:P04637)"
  )
  ann_search_p.add_argument(
      "--geneProductSubset", help="Gene product subset (e.g. Swiss-Prot)"
  )
  ann_search_p.add_argument(
      "--geneProductType", help="Gene product type (e.g. protein)"
  )
  ann_search_p.add_argument("--goId", help="GO ID (e.g. GO:0006915)")
  ann_search_p.add_argument(
      "--taxonId", type=int, help="NCBI Taxon ID (e.g. 9606 for human)"
  )
  ann_search_p.add_argument(
      "--evidenceCode", help="Evidence code (e.g. ECO:0000269 for EXP)"
  )
  ann_search_p.add_argument(
      "--goUsage", choices=["exact", "slim", "desc"], help="How to use goId"
  )
  ann_search_p.add_argument(
      "--qualifier", help="Qualifier (e.g. enables, part_of, involved_in)"
  )
  ann_search_p.add_argument(
      "--aspect",
      choices=[
          "biological_process",
          "molecular_function",
          "cellular_component",
      ],
      help="GO aspect",
  )
  ann_search_p.add_argument(
      "--limit", type=int, default=25, help="Max results per page (max 100)"
  )
  ann_search_p.add_argument("--page", type=int, default=1, help="Page number")
  ann_search_p.add_argument("--output", required=True, help="Output JSON file")
  ann_search_p.set_defaults(func=annotation_search)

  # Gene Product Subcommands
  gp_parser = subparsers.add_parser(
      "geneproduct", help="Gene Product search endpoints"
  )
  gp_sub = gp_parser.add_subparsers(dest="gp_cmd", required=True)

  gp_search_p = gp_sub.add_parser("search", help="Search gene products")
  gp_search_p.add_argument(
      "--query", required=True, help="Query string (e.g. PROC)"
  )
  gp_search_p.add_argument(
      "--taxonId", type=int, help="NCBI Taxon ID (e.g. 9606 for human)"
  )
  gp_search_p.add_argument(
      "--limit", type=int, default=25, help="Max results per page (max 100)"
  )
  gp_search_p.add_argument("--page", type=int, default=1, help="Page number")
  gp_search_p.add_argument("--output", required=True, help="Output JSON file")
  gp_search_p.set_defaults(func=geneproduct_search)

  args = parser.parse_args()
  args.func(args)


if __name__ == "__main__":
  main()

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

Security

What our scanner noticed· Low risk

  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

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

AutoGineer is not affiliated with or endorsed by Google DeepMind.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download quickgo-database.zip