NCBI GEO Database Query
SkillDatabases & dataQuery NCBI GEO for gene expression datasets. Use when user asks about RNA-seq datasets, microarray data, expression data, GEO accessions, or finding public datasets. Triggers on "geo", "gene expression omnibus", "expression dataset", "RNA-seq dataset", "microarray dataset", "GSE", "GDS".
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the NCBI GEO Database Query skill
What this skill tells your AI
The instructions your AI receives, as published by biotender-max/awesome-bio-agent-skills in skills/bioclaw/query-geo/SKILL.md and read by ahel’s review.
Query Gene Expression Omnibus for public expression datasets.
When to Use
- User wants to find RNA-seq or microarray datasets
- User asks about gene expression studies for a disease/tissue
- User provides a GEO accession (GSE/GDS) to look up
- User wants to download expression data
How to Execute
from Bio import Entrez
import json
Entrez.email = "bioclaw@example.com"
# 1. Search GEO datasets
def search_geo(query, max_results=10, db="gds"):
handle = Entrez.esearch(db=db, term=query, retmax=max_results, sort="relevance")
record = Entrez.read(handle)
handle.close()
return record
# 2. Get dataset summaries
def geo_summary(id_list, db="gds"):
ids = ",".join(str(i) for i in id_list)
handle = Entrez.esummary(db=db, id=ids, retmode="json")
result = json.loads(handle.read())
handle.close()
return result
# 3. Search for Series (GSE)
def search_gse(keyword, organism="Homo sapiens", max_results=10):
query = f'"{keyword}" AND "{organism}"[Organism] AND gse[ETYP]'
return search_geo(query, max_results)
# Example: Find breast cancer RNA-seq datasets
search = search_gse("breast cancer RNA-seq", max_results=5)
print(f"Found {search['Count']} datasets")
if search['IdList']:
summaries = geo_summary(search['IdList'])
for uid in search['IdList']:
info = summaries['result'].get(str(uid), {})
title = info.get('title', 'N/A')
gse = info.get('accession', 'N/A')
gpl = info.get('gpl', 'N/A')
n_samples = info.get('n_samples', 'N/A')
summary = info.get('summary', 'N/A')[:200]
print(f"\n{gse}: {title}")
print(f" Platform: {gpl}, Samples: {n_samples}")
print(f" Summary: {summary}...")
print(f" URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc={gse}")
Search Syntax
- By keyword:
"CRISPR" AND gse[ETYP] - By organism:
"Homo sapiens"[Organism] - By platform:
"Illumina"[Platform] - By date:
"2024/01:2026/12"[PDAT] - Combine:
"breast cancer" AND "RNA-seq" AND "Homo sapiens"[Organism] AND gse[ETYP]
Follow-up Suggestions
- "Want me to download the expression matrix for this dataset?"
- "Should I do differential expression analysis?"
- "Want me to check what genes are differentially expressed?"
Signals
- GitHub stars
- 178
- Forks
- 32
- Last commit
- Jul 2026
Advanced
- Catalog kind
- skill
- Gateway key
query-geo- Source
- github.com/biotender-max/awesome-bio-agent-skills