genomic-feature-annotation

Annotate genomic regions with gene-based features using Homer or ChIPseeker.

12|3|Updated Nov 4, 2025

Search Tags:#visualization #BED #genomic-annotation #genomic-features #Homer #ChIPseeker

Ready to run

npx skills add https://github.com/BIsnake2001/ChromSkills --skill genomic-feature-annotation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: genomic-feature-annotation
Source: https://github.com/BIsnake2001/ChromSkills/tree/main/10_toolBased.genomic-feature-annotation
Command: npx skills add https://github.com/BIsnake2001/ChromSkills --skill genomic-feature-annotation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Annotate genomic region files (e.g., BED, peaks) with gene-based features using Homer or ChIPseeker, producing annotated regions and feature-distribution visuals to help interpret regulatory landscapes.

Core Features & Use Cases

Supports input formats such as BED, narrowPeak, and broadPeak.
Lets you choose between Homer and ChIPseeker workflows for annotation, producing results in standard formats and plots.
Outputs include annotated region files, summary statistics, and visualization plots; use cases include identifying TF target genes or annotating enhancer regions.
ChIPseeker mode provides R-based annotation workflows with TxDb and orgDb databases; Homer mode uses annotatePeaks.pl with customizable options.

Quick Start

Provide input genomic region files (BED/narrowPeak/broadPeak) and a genome assembly, then choose Homer or ChIPseeker to run annotations and produce annotated results with visualizations.

Frequently Asked Questions about genomic-feature-annotation

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

FAQPage Schema

How do I annotate BED files with genomic features using Homer or ChIPseeker?▼

To annotate genomic regions, you provide BED, narrowPeak, or broadPeak files alongside an explicit genome assembly and sample names. The Skill processes these inputs through Homer or ChIPseeker workflows to produce annotated region files and feature-distribution plots.

Can I use ChIPseeker to annotate TF peak sets and generate visualization plots?▼

Yes, ChIPseeker mode provides an R-based annotation workflow for TF peak sets using TxDb and orgDb databases. It generates annotated region files, summary statistics, and visualization plots to help interpret regulatory landscapes.

What input formats and genome assemblies are supported for genomic annotation?▼

Genomic annotation supports BED, narrowPeak, and broadPeak input formats across various species. You must explicitly specify the genome assembly, input region files, and sample naming to ensure reproducible results.

What is the best way to identify TF target genes from peak sets?▼

Annotating genomic regions with gene-based features is the best way to identify TF target genes. By processing peak sets through Homer or ChIPseeker, you get annotated region files and feature-distribution visuals for interpreting regulatory landscapes.

Does Homer annotation require specific settings for reproducible results?▼

Yes, Homer mode uses annotatePeaks.pl with customizable options, and generating reproducible results requires explicit genome assembly, input region files, sample naming, and optional Homer annotation settings.

Why do I need to specify sample naming for genomic region annotation?▼

Explicit sample naming is required alongside input region files and genome assembly to enable reproducible results. It ensures annotated output files and visualization plots are correctly tracked across Homer or ChIPseeker workflows.