Can I download raw sequencing data with this skill?

Yes, the skill includes patterns for accessing supplementary files and SRA data links via FTP and programmatic downloaders.

Does it support GSE and GSM accession types?

Yes, it provides specific methods for handling Series (GSE), Samples (GSM), and Platforms (GPL) accession hierarchies.

Which Python libraries does this skill recommend?

It primarily utilizes Biopython (Entrez) for searching the database and GEOparse for downloading and handling the data files.

What is the GEO Database skill used for?

This skill provides specialized guidance for accessing the NCBI Gene Expression Omnibus to find and download gene expression datasets for bioinformatics research.

Is an email address required for NCBI searches?

Yes, when using Entrez via Biopython as shown in the skill, providing an email address is required by NCBI to monitor usage and contact users if necessary.

GEO Database Explorer

Name: GEO Database Explorer
Author: Activer007

byActiver007

Ciencia de Datos y ML

Accesses and analyzes NCBI Gene Expression Omnibus (GEO) datasets for transcriptomics and genomics research.

Acerca de

The GEO Database skill enables seamless interaction with the NCBI Gene Expression Omnibus, the world's largest public repository for functional genomics data. It provides standardized implementation patterns for searching and retrieving Series (GSE), Samples (GSM), Platforms (GPL), and DataSets (GDS) using Python libraries like Biopython and GEOparse. This skill is essential for bioinformaticians and researchers who need to automate the downloading of SOFT/Matrix files, manage complex experimental metadata, and transform raw genomic records into analysis-ready pandas DataFrames for differential expression or meta-analysis workflows.

Características Principales

0 GitHub stars
Support for both microarray and high-throughput RNA-seq data types
Programmatic search of NCBI GEO datasets using Entrez E-utilities
Direct FTP access patterns for bulk genomic data downloads
Parsing of SOFT and Matrix files into structured pandas DataFrames
Automated retrieval of Series (GSE) and Sample (GSM) metadata

Casos de Uso

Retrieving probe annotations and platform details for microarray data normalization
Automating the collection of RNA-seq data for specific organisms and tissue types
Meta-analysis of gene expression datasets across different disease conditions

Acerca de

Características Principales

0 GitHub stars
Support for both microarray and high-throughput RNA-seq data types
Programmatic search of NCBI GEO datasets using Entrez E-utilities
Direct FTP access patterns for bulk genomic data downloads
Parsing of SOFT and Matrix files into structured pandas DataFrames
Automated retrieval of Series (GSE) and Sample (GSM) metadata

Casos de Uso

Retrieving probe annotations and platform details for microarray data normalization
Automating the collection of RNA-seq data for specific organisms and tissue types
Meta-analysis of gene expression datasets across different disease conditions