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geo-database

Retrieve and parse GEO datasets using GEOparse and Biopython.

1|Updated Jan 26, 2026
Search Tags:#bioinformatics#data retrieval#GEO#datasets#GEOparse#expression data
One-click install
npx skills add https://github.com/Victory-Hugo/S2-Agent-Skill --skill geo-database-victory-hugo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: geo-database
Source: https://github.com/Victory-Hugo/S2-Agent-Skill/tree/main/skills/databases/geo-database
Command: npx skills add https://github.com/Victory-Hugo/S2-Agent-Skill --skill geo-database-victory-hugo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables researchers to search, access, and retrieve GEO datasets and metadata from NCBI in a programmatic way, streamlining data discovery and integration into analysis pipelines.

Core Features & Use Cases

  • GEO data discovery: Locate studies by accession, title, organism, data type, and publication date.
  • Data retrieval formats: Download and parse SOFT files, series matrices, and MINiML/XML records, including supplementary materials.
  • Cross-study workflows: Build reproducible pipelines for downloading multiple series and aggregating expression data for meta-analyses.
  • Use Case: Retrieve a GEO Series matrix for a chosen study, map probes to genes with platform annotations, and load the data into your analysis notebook.

Quick Start

Use the geo-database skill to fetch the series matrix for a GEO Series (e.g., GSE123456) and load it into your analysis workflow.

Frequently Asked Questions about geo-database

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I programmatically retrieve GEO expression data from NCBI?▼

Programmatically retrieve GEO expression data by using Python libraries like GEOparse and Biopython to download and parse series matrices, SOFT files, or MINiML records directly from NCBI into your workflow.

What's the best way to download multiple GEO series matrices for meta-analysis?▼

Build reproducible cross-study pipelines by programmatically iterating through multiple GEO Series accession numbers to download and aggregate their series matrices and expression data for meta-analyses.

How do I parse SOFT files and map probes to genes in a Python notebook?▼

Parse SOFT files by loading the retrieved GEO dataset with GEOparse, then map probes to genes using platform annotations to load the expression data into your analysis notebook.

Can I discover GEO datasets by organism and publication date without manual searching?▼

Discover GEO datasets programmatically by filtering studies based on organism, data type, publication date, title, or accession number to streamline data discovery and integration tasks.

Does GEOparse support downloading MINiML and supplementary materials from GEO?▼

GEOparse supports downloading and parsing MINiML/XML records along with SOFT files and series matrices, including supplementary materials required for cross-study bioinformatics workflows.

What are the limitations of using GEOparse for cross-study comparisons?▼

GEOparse focuses on basic processing and retrieval of GEO datasets, so advanced cross-study comparisons require external pipelines to aggregate expression data and map probes consistently across different platforms.

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