NCBI GEO Database Query

SkillDatabases & data

Query 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.

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