skills/43-wentorai-research-plugins/skills/domains/biomedical/quickgo-api/SKILL.md
Browse and search Gene Ontology annotations via the QuickGO API
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research quickgo-apiInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
QuickGO is the EBI's fast browser and API for Gene Ontology (GO) annotations — the standard framework for describing gene/protein functions across all organisms. It provides access to 800M+ GO annotations covering biological processes, molecular functions, and cellular components. Essential for functional genomics, pathway analysis, and gene set enrichment. Free, no authentication.
https://www.ebi.ac.uk/QuickGO/services
# Search terms by keyword
curl "https://www.ebi.ac.uk/QuickGO/services/ontology/go/search?query=apoptosis&limit=20"
# Get term details
curl "https://www.ebi.ac.uk/QuickGO/services/ontology/go/terms/GO:0006915"
# Get term ancestors/descendants
curl "https://www.ebi.ac.uk/QuickGO/services/ontology/go/terms/GO:0006915/ancestors"
curl "https://www.ebi.ac.uk/QuickGO/services/ontology/go/terms/GO:0006915/descendants"
# Get annotations for a protein (UniProt ID)
curl "https://www.ebi.ac.uk/QuickGO/services/annotation/search?geneProductId=P04637&limit=50"
# Annotations for a GO term
curl "https://www.ebi.ac.uk/QuickGO/services/annotation/search?goId=GO:0006915&taxonId=9606&limit=50"
# Filter by evidence code
curl "https://www.ebi.ac.uk/QuickGO/services/annotation/search?\
goId=GO:0006915&taxonId=9606&evidence=EXP,IDA,IMP&limit=50"
# Filter by aspect (ontology branch)
curl "https://www.ebi.ac.uk/QuickGO/services/annotation/search?\
geneProductId=P04637&aspect=biological_process"
# Download as TSV
curl "https://www.ebi.ac.uk/QuickGO/services/annotation/downloadSearch?\
goId=GO:0006915&taxonId=9606&downloadLimit=10000" -o annotations.tsv
| Aspect | Code | Description |
|--------|------|-------------|
| Biological Process | biological_process | What the gene does |
| Molecular Function | molecular_function | Biochemical activity |
| Cellular Component | cellular_component | Where in the cell |
| Code | Meaning | Reliability |
|------|---------|-------------|
| EXP | Inferred from Experiment | High |
| IDA | Inferred from Direct Assay | High |
| IMP | Inferred from Mutant Phenotype | High |
| IPI | Inferred from Physical Interaction | Medium |
| ISS | Inferred from Sequence Similarity | Medium |
| IEA | Inferred from Electronic Annotation | Lower |
import requests
BASE_URL = "https://www.ebi.ac.uk/QuickGO/services"
def search_go_terms(query: str, limit: int = 20) -> list:
"""Search Gene Ontology terms."""
resp = requests.get(
f"{BASE_URL}/ontology/go/search",
params={"query": query, "limit": limit},
)
resp.raise_for_status()
data = resp.json()
results = []
for term in data.get("results", []):
results.append({
"id": term.get("id"),
"name": term.get("name"),
"aspect": term.get("aspect"),
"definition": term.get("definition", {}).get("text", ""),
})
return results
def get_protein_annotations(uniprot_id: str,
aspect: str = None,
experimental_only: bool = False) -> list:
"""Get GO annotations for a protein."""
params = {"geneProductId": uniprot_id, "limit": 100}
if aspect:
params["aspect"] = aspect
if experimental_only:
params["evidence"] = "EXP,IDA,IMP,IPI,IGI,IEP"
resp = requests.get(
f"{BASE_URL}/annotation/search",
params=params,
)
resp.raise_for_status()
data = resp.json()
annotations = []
for ann in data.get("results", []):
annotations.append({
"go_id": ann.get("goId"),
"go_name": ann.get("goName"),
"aspect": ann.get("goAspect"),
"evidence": ann.get("goEvidence"),
"reference": ann.get("reference"),
})
return annotations
def get_term_genes(go_id: str, taxon_id: int = 9606,
limit: int = 100) -> list:
"""Get genes annotated with a GO term."""
params = {
"goId": go_id,
"taxonId": taxon_id,
"limit": limit,
}
resp = requests.get(
f"{BASE_URL}/annotation/search",
params=params,
)
resp.raise_for_status()
data = resp.json()
genes = set()
for ann in data.get("results", []):
genes.add(ann.get("geneProductId", ""))
return sorted(genes)
# Example: search for apoptosis-related GO terms
terms = search_go_terms("programmed cell death")
for t in terms[:5]:
print(f"{t['id']}: {t['name']} ({t['aspect']})")
# Example: get p53 protein annotations
annotations = get_protein_annotations("P04637",
experimental_only=True)
for a in annotations[:10]:
print(f" {a['go_id']} {a['go_name']} [{a['evidence']}]")
tools
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文(用于「文献综述工具选型」与「一键安装/运行」)。
development
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
documentation
Use when the project collects primary data or runs a field, lab, or survey experiment, before the intervention begins — write the pre-analysis plan, size the sample from a power calculation, and register with the AEA RCT Registry. Apply after the design is chosen in aer-identification and before any outcome data are seen.
tools
Guide economists to authoritative data sources with explicit, confirmed data specifications before retrieval; interfaces with Playwright MCP to navigate portals and extract real data, not articles about data.