scientific-gene-expression-transcriptomics

Automate bulk RNA-seq analysis from GEO retrieval to GSEA enrichment interpretation.

3|1|Updated Feb 11, 2026

Search Tags:#differential-expression #gsea #deseq2 #transcriptomics #eqtl #geo-datasets #bulk-rna-seq

Ready to run

npx skills add https://github.com/nahisaho/satori --skill scientific-gene-expression-transcriptomics

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-gene-expression-transcriptomics
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-gene-expression-transcriptomics
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-gene-expression-transcriptomics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides an end-to-end workflow for bulk RNA-seq transcriptomics, integrating data retrieval from GEO, preprocessing, differential expression analysis (via PyDESeq2), GTEx reference querying, eQTL lookups, and gene-set enrichment (GSEA/ORA) to enable rapid, reproducible interpretation of transcriptomic studies.

Core Features & Use Cases

GEO dataset retrieval and preprocessing for bulk RNA-seq and microarray data.
Differential expression analysis using DESeq2 (via PyDESeq2) and downstream enrichment analyses (GSEA/ORA).
GTEx tissue expression context and eQTL lookups to help interpret tissue-specific patterns.
Expression Atlas integration for baseline and differential expression exploration.
End-to-end pipeline outputs including ranked gene lists, enrichment reports, and publication-ready plots.

Quick Start

Run the end-to-end bulk RNA-seq pipeline to fetch GEO data, preprocess counts, perform differential expression with PyDESeq2, and interpret results with GTEx references and GSEA.

Frequently Asked Questions about scientific-gene-expression-transcriptomics

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

FAQPage Schema

How do I perform end-to-end bulk RNA-seq differential expression analysis from GEO datasets?▼

You can automate bulk RNA-seq differential expression by retrieving GEO datasets, preprocessing counts, and running PyDESeq2 to output ranked gene lists and publication-ready plots.

Can I use DESeq2 for differential expression in Python without R?▼

Yes, PyDESeq2 enables differential expression analysis directly in Python, providing ranked gene lists and visualizations while avoiding R environment dependencies.

How do I add tissue expression context and eQTL lookups to my transcriptomics pipeline?▼

You can integrate GTEx tissue expression context and eQTL lookups into your bulk RNA-seq pipeline to interpret tissue-specific patterns alongside differential expression results.

What's the best way to run GSEA and ORA on differential expression results?▼

Use gseapy to perform GSEA and ORA on your PyDESeq2 differential expression outputs, generating comprehensive enrichment reports for bulk RNA-seq experiments.

Do I need specific Python libraries to run this transcriptomics analysis pipeline?▼

Yes, the pipeline requires GEOparse for data retrieval, PyDESeq2 for differential expression, gseapy for enrichment, plus standard plotting and data manipulation libraries.

When should I use this bulk RNA-seq pipeline instead of analyzing microarray data separately?▼

This pipeline handles both bulk RNA-seq and microarray data retrieval from GEO, but its PyDESeq2 differential expression and GTEx integration features are specifically optimized for bulk RNA-seq transcriptomics studies.