Quickgo Database
Google DeepMindOfficialPublished by Google DeepMind
- Skill
- Tool integration
- Science and research
- Apache-2.0
- Low risk
- Has scripts
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
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google-deepmind/quickgo-database/downloadUnzip 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
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.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 100and the--pageparameter for larger datasets.Output Files: Always use the
--outputflag to save responses to a file incrementally or parse viajq.Evidence Codes: Prioritize experimental evidence (
ECO:0000269) over electronic (ECO:0000501) to avoid noisy predictions.Taxon Filtering: Use
--taxonId 9606to 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:
go: For retrieving information about GO terms (e.g. definitions, ancestors, descendants, and slims). See references/go_terms.md.annotation: For finding functional annotations linking gene products to GO terms. This is your primary functional mapper. See references/annotations.md.geneproduct: For resolving gene symbols (likePROC) to their formal database identifiers. See references/gene_products.md.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.json2. 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.json3. 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.json4. 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.jsonFiles
- ATTRIBUTION.mdAdded by AutoGineer914 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md5.5 KiB
- references/annotations.md2.0 KiB
- references/citation.bib1.4 KiB
- references/eco_terms.md1.4 KiB
- references/gene_products.md849 B
- references/go_terms.md2.0 KiB
- scripts/quickgo_tool.pyScript9.7 KiB
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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/quickgo_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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