How does this skill help with RNA-seq data?

It provides code snippets and logic for normalization, log-transformation, and statistical testing to identify significant gene expression changes across samples.

Can it help with gene naming conventions?

Yes, it provides specific guidance on HGNC symbols for human genes and the distinct capitalization rules required for mouse gene nomenclature.

What visualizations are included in this skill?

The skill provides templates for generating Volcano plots to visualize significance vs. fold change, as well as community-based co-expression network graphs.

Does it support pathway analysis?

Yes, it includes implementation patterns for Fisher's Exact Test to perform enrichment analysis against Gene Ontology (GO) and KEGG pathway databases.

Genomics Analysis

Name: Genomics Analysis
Author: justaddcoffee

byjustaddcoffee

Data Science & ML

Provides specialized strategies and code patterns for genomics and transcriptomics data analysis, visualization, and biological interpretation.

About

The Genomics skill equips Claude with domain-specific knowledge to assist in complex biological data workflows, focusing on RNA-seq and gene expression measurements. It provides implementation patterns for differential expression analysis, pathway enrichment using Gene Ontology (GO) and KEGG, and gene nomenclature standardization. This skill is ideal for bioinformaticians and researchers needing help with statistical testing, multiple testing correction (FDR), and generating visualizations like volcano plots to derive meaningful biological insights from large-scale molecular datasets.

Key Features

0 GitHub stars
Gene Set and Pathway Enrichment (GO, KEGG, Fisher's Exact)
Quality control checklists for batch effects and normalization
Standardized nomenclature guidance for Human and Mouse genes
Differential expression analysis workflows using Python
Data visualization patterns including Volcano plots and Network graphs

Use Cases

Performing Gene Ontology (GO) enrichment to interpret the function of gene clusters
Visualizing gene co-expression networks to identify transcription factor activity
Analyzing RNA-seq counts to identify differentially expressed genes between groups

About

Key Features

0 GitHub stars
Gene Set and Pathway Enrichment (GO, KEGG, Fisher's Exact)
Quality control checklists for batch effects and normalization
Standardized nomenclature guidance for Human and Mouse genes
Differential expression analysis workflows using Python
Data visualization patterns including Volcano plots and Network graphs

Use Cases

Performing Gene Ontology (GO) enrichment to interpret the function of gene clusters
Visualizing gene co-expression networks to identify transcription factor activity
Analyzing RNA-seq counts to identify differentially expressed genes between groups