How does it handle different species?

The skill provides specific guidance on nomenclature for human (HGNC) and mouse gene symbols to ensure accurate data interpretation and adherence to publication standards.

Can this skill help with biological pathway analysis?

Yes, it provides implementation patterns for Gene Ontology (GO) and KEGG pathway enrichment using statistical tests like Fisher's Exact Test.

Does it handle multiple testing correction?

Yes, the included workflows use the Benjamini-Hochberg FDR method to adjust p-values, ensuring statistically sound findings across thousands of genes.

What types of genomic data does this skill support?

It supports RNA-seq counts, microarray intensities, and single-cell RNA-seq expression data, providing normalization and analysis strategies for each.

Genomics & Transcriptomics Analysis

Name: Genomics & Transcriptomics Analysis
Author: justaddcoffee

byjustaddcoffee

Ciencia de Datos y ML

Provides strategies, implementation patterns, and workflows for genomics and transcriptomics data analysis.

Acerca de

This skill equips Claude with specialized knowledge for biological data science, specifically focusing on gene expression analysis and transcriptomics. It provides robust patterns for processing RNA-seq and microarray data, performing differential expression analysis using Python, and conducting pathway enrichment using GO and KEGG databases. It also includes guidance on gene nomenclature, visualization techniques like volcano plots, and multi-omic integration, making it an essential tool for researchers and bioinformaticians using Claude Code for scientific discovery.

Características Principales

0 GitHub stars
Pathway and Gene Set Enrichment Analysis (GSEA) implementation
Automated differential gene expression (DGE) analysis workflows
Cross-species gene nomenclature and symbol verification
Visualization tools for biological data including volcano plots
Co-expression network construction and community detection

Casos de Uso

Integrating transcriptomic results with metabolomic or proteomic datasets
Interpreting biological pathways affected by experimental conditions
Analyzing RNA-seq count data to identify significant biomarkers

Acerca de

Características Principales

0 GitHub stars
Pathway and Gene Set Enrichment Analysis (GSEA) implementation
Automated differential gene expression (DGE) analysis workflows
Cross-species gene nomenclature and symbol verification
Visualization tools for biological data including volcano plots
Co-expression network construction and community detection

Casos de Uso

Integrating transcriptomic results with metabolomic or proteomic datasets
Interpreting biological pathways affected by experimental conditions
Analyzing RNA-seq count data to identify significant biomarkers