/quickgo_database

name: quickgo-database
description: >
  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).

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