de-summary

Summarize differential expression results from DESeq2, edgeR, or limma outputs.

Updated May 10, 2026

Search Tags:#summary #interpretation #differential-expression #transcriptomics #biological themes

Ready to run

npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill de-summary

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: de-summary
Source: https://github.com/MubasherMohammed/opencode-BioInfo/tree/main/python/skills/de-summary
Command: npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill de-summary

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a structured and interpretable summary of pre-computed differential expression results, streamlining the process from raw data to ready-for-publication results.

Core Features & Use Cases

Automated DE Results Summarization: Automatically summarize the most significant differentially expressed genes, grouped by biological themes, and key observations.
Publication-Ready Interpretations: Generate a detailed, publication-ready interpretation of the DE results that includes biological themes and observations about the DE landscape.
Use Case: Imagine you have conducted a differential expression analysis and need to generate a summary that is ready for inclusion in a scientific publication.

Quick Start

Run 'de-summary --input your_de_results.csv' to generate a DE results summary from your differential expression output table.

Frequently Asked Questions about de-summary

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

FAQPage Schema

How do I summarize differential expression results for publication?▼

Differential expression results summarization interprets DESeq2, edgeR, or limma outputs, ranking top genes by padj and log2FoldChange to produce a structured, publication-ready summary of biological themes.

What is the best way to interpret differentially expressed genes automatically?▼

Interpreting differentially expressed genes involves ranking significant genes by padj and log2FoldChange values, grouping them into biological themes, and generating a machine-readable summary of key transcriptomics observations.

Can I use this with DESeq2, edgeR, or limma output tables?▼

Yes, it works with pre-computed differential expression output tables from DESeq2, edgeR, or limma, requiring only a CSV input of your results to generate the summary.

Does summarizing DE results require any additional dependencies?▼

No additional dependencies are required; simply provide your pre-computed differential expression CSV file to generate the biological themes and publication-ready interpretation.

What are the limitations of automated DE results interpretation?▼

Automated DE results interpretation requires pre-computed differential expression tables as input and does not perform the statistical testing itself, focusing solely on ranking genes and identifying biological themes.