Verified:GitHub

genomics

Guide genomics data analysis decisions for RNA-seq, microarray, and single-cell data.

13|Updated Dec 16, 2025

Search Tags:#genomics #differential-expression #pathway-analysis #variant-interpretation #transcriptomics #gene-set-enrichment

Ready to run

npx skills add https://github.com/justaddcoffee/open-science-skills --skill genomics

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: genomics
Source: https://github.com/justaddcoffee/open-science-skills/tree/main/genomics
Command: npx skills add https://github.com/justaddcoffee/open-science-skills --skill genomics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Genomics data analysis decisions can be complex due to multiple data types, evolving best practices, and the need for consistent interpretation across studies.

Core Features & Use Cases

Best-practice workflows for differential expression analysis, gene set enrichment, and interpretation of genetic variants.
Guidance on data types (bulk RNA-seq, microarray, single-cell) and matching analytical strategies.
Examples illustrating end-to-end workflows, interpretation steps, and result reporting to support reproducible analyses.

Quick Start

Use the genomics skill to structure your analysis plan for RNA-seq differential expression discovery.

Frequently Asked Questions about genomics

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

FAQPage Schema

What is the best way to structure RNA-seq differential expression analysis workflows?▼

Pathway enrichment and gene set enrichment analysis workflows are guided by structured templates that provide interpretation steps and best-practice reporting, ensuring consistent and reproducible results across different genomic studies.

How do I match analytical strategies for different genomics data types like microarray and single-cell?▼

Matching analytical strategies for genomics data types like microarray and single-cell requires guidance on data type characteristics, enabling the selection of appropriate workflows for bulk RNA-seq, microarray, and single-cell transcriptomics analyses.

Can I use structured workflows for variant interpretation across different genomics studies?▼

Variant interpretation across different genomics studies can be performed using structured workflows that provide consistent analytical decisions, best-practice guidance, and reproducible example templates to inform the interpretation process.

Why does genomics data analysis require consistent interpretation across multiple data types?▼

Genomics data analysis requires consistent interpretation across multiple data types due to evolving best practices and the complexity of applying appropriate analytical strategies to bulk RNA-seq, microarray, and single-cell transcriptomics data.

Do I need reproducible example templates to perform gene set enrichment analysis?▼

Reproducible example templates are needed to perform gene set enrichment analysis because they illustrate end-to-end workflows, interpretation steps, and result reporting, which directly support reproducible genomic analyses.