tools-only ChIP-seq Visualization - Usage Guide
Overview
Create heatmaps, profile plots, and genome browser-style visualizations for ChIP-seq data using deepTools (CLI), ChIPseeker (R), and Gviz (R).
Prerequisites
# deepTools (CLI)
conda install -c bioconda deeptools
# R packages
# BiocManager::install(c('ChIPseeker', 'Gviz', 'EnrichedHeatmap'))
Quick Start
Tell your AI agent what you want to do:
- "Create a heatmap of ChIP-seq signal around TSS regions"
- "Generate profile plots comparing treatment vs control"
- "Make a genome browser view of my peaks at the MYC locus"
Example Prompts
Heatmaps and Profiles
"Create a heatmap showing H3K27ac signal at all promoters"
"Compare ChIP-seq signal profiles between wild-type and knockout samples"
"Generate a reference-point matrix centered on peak summits"
Genome Browser Views
"Create a publication-quality browser track for chromosome 1:1-2Mb"
"Visualize my bigWig files alongside gene annotations"
Normalization
"Convert my BAM files to bigWig with CPM normalization"
"Create RPGC-normalized coverage tracks for my samples"
What the Agent Will Do
- Convert BAM files to bigWig format with appropriate normalization (CPM, RPKM, RPGC)
- Compute signal matrices around reference points (TSS, peak summits) or scaled regions
- Generate heatmaps and profile plots using deepTools or EnrichedHeatmap
- Create genome browser visualizations with Gviz for specific loci
- Customize plot aesthetics and export publication-ready figures
Tips
- Use CPM normalization for comparing samples with similar library sizes
- RPGC (reads per genomic content) is better for comparing samples with different sequencing depths
- For TSS profiles, use a window of +/- 3kb around the TSS
- deepTools is faster for large-scale analysis; R packages offer more customization
- Always include a control/input track when visualizing ChIP-seq data