Functional Enrichment Analysis FAQs

Question 1

How does this skill improve a bioinformatics workflow?

Accepted Answer

It streamlines the transition from raw genomic intervals to publication-quality figures. It handles genome assembly validation, chromosome naming standardization, and statistical enrichment in one automated flow, saving hours of manual coding.

Question 2

Which genomic formats and species are supported?

Accepted Answer

It supports standard formats including BED, narrowPeak, and broadPeak, as well as simple gene lists. It works with any genome assembly supported by HOMER, such as hg38, mm10, and danRer11.

Question 3

What does the Functional Enrichment Analysis skill do?

Accepted Answer

This skill automates the biological interpretation of genomic data. It maps genomic coordinates (like BED or narrowPeak files) to genes and performs Gene Ontology (GO) and KEGG pathway enrichment analysis using the HOMER suite, concluding with automated R-based visualizations.

Question 4

What visualization capabilities does it provide?

Accepted Answer

The skill generates high-quality R-based barplots and dotplots using ggplot2. These visualizations highlight the most significantly enriched biological processes, molecular functions, and KEGG pathways for your dataset.

Question 5

When should I use this skill in Claude Code?

Accepted Answer

Use this skill when you have high-throughput sequencing results (like ChIP-seq or RNA-seq) and need to identify the functional significance of specific genomic regions or gene sets without manual data processing.

Functional Enrichment Analysis

Functional Enrichment Analysis

Key Features

Use Cases

Key Features

Use Cases