install
source · Clone the upstream repo
git clone https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- "$T" && mkdir -p ~/.claude/skills && cp -r "$T/Skills/Epigenomics/chip-seq/peak-annotation" ~/.claude/skills/mdbabumiamssm-llms-universal-life-science-and-clinical-skills-peak-annotation && rm -rf "$T"
manifest:
Skills/Epigenomics/chip-seq/peak-annotation/SKILL.mdsource content
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# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# All Rights Reserved.
#
# This code is proprietary and confidential.
# Unauthorized copying of this file, via any medium is strictly prohibited.
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name: bio-chipseq-peak-annotation description: Annotate ChIP-seq peaks to genomic features and genes using ChIPseeker. Assign peaks to promoters, exons, introns, and intergenic regions. Find nearest genes and calculate distance to TSS. Generate annotation plots and statistics. Use when annotating ChIP-seq peaks to genomic features. tool_type: r primary_tool: ChIPseeker measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
Peak Annotation with ChIPseeker
Load Peaks and Annotations
library(ChIPseeker) library(TxDb.Hsapiens.UCSC.hg38.knownGene) library(org.Hs.eg.db) txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene # Read peaks from MACS3 peaks <- readPeakFile('sample_peaks.narrowPeak')
Annotate Peaks
# Annotate with default settings peak_anno <- annotatePeak( peaks, TxDb = txdb, annoDb = 'org.Hs.eg.db' ) # View annotation summary peak_anno
Custom Promoter Definition
# Define promoter region (-3kb to +3kb from TSS) peak_anno <- annotatePeak( peaks, TxDb = txdb, tssRegion = c(-3000, 3000), # Promoter definition annoDb = 'org.Hs.eg.db' )
Extract Annotated Data Frame
# Convert to data frame anno_df <- as.data.frame(peak_anno) # Key columns: seqnames, start, end, annotation, distanceToTSS, SYMBOL, GENENAME head(anno_df) # Export to CSV write.csv(anno_df, 'annotated_peaks.csv', row.names = FALSE)
Get Genes with Peaks in Promoter
# Filter for promoter peaks promoter_peaks <- anno_df[grep('Promoter', anno_df$annotation), ] # Get unique genes promoter_genes <- unique(promoter_peaks$SYMBOL)
Annotation Pie Chart
# Pie chart of genomic feature distribution plotAnnoPie(peak_anno) # Bar plot alternative plotAnnoBar(peak_anno)
Distance to TSS Plot
# Distribution of peaks relative to TSS plotDistToTSS(peak_anno, title = 'Distribution of peaks relative to TSS')
Compare Multiple Peak Sets
# Read multiple peak files peak_files <- list( H3K4me3 = 'H3K4me3_peaks.narrowPeak', H3K27ac = 'H3K27ac_peaks.narrowPeak', H3K27me3 = 'H3K27me3_peaks.broadPeak' ) peak_list <- lapply(peak_files, readPeakFile) # Annotate all anno_list <- lapply(peak_list, annotatePeak, TxDb = txdb, annoDb = 'org.Hs.eg.db') # Compare annotations plotAnnoBar(anno_list) plotDistToTSS(anno_list)
Venn Diagram of Peak Overlap
# Find overlapping peaks genes_list <- lapply(anno_list, function(x) as.data.frame(x)$SYMBOL) vennplot(genes_list)
Coverage Plot
# Plot peak coverage around TSS covplot(peaks, weightCol = 'V5') # V5 is score column in narrowPeak
Profile Heatmap Around TSS
# Get promoter coordinates promoter <- getPromoters(TxDb = txdb, upstream = 3000, downstream = 3000) # Get tag matrix tagMatrix <- getTagMatrix(peaks, windows = promoter) # Plot heatmap tagHeatmap(tagMatrix, xlim = c(-3000, 3000), color = 'red') # Average profile plotAvgProf(tagMatrix, xlim = c(-3000, 3000), xlab = 'Distance from TSS')
Functional Enrichment of Peak Genes
library(clusterProfiler) # Get genes from peaks genes <- unique(anno_df$ENTREZID) # GO enrichment ego <- enrichGO( gene = genes, OrgDb = org.Hs.eg.db, ont = 'BP', pAdjustMethod = 'BH', pvalueCutoff = 0.05 )
Seq2Gene - All Genes in Peak Regions
# Find all genes overlapping peak regions (not just nearest) genes_in_peaks <- seq2gene(peaks, tssRegion = c(-1000, 1000), flankDistance = 3000, TxDb = txdb)
Different Organisms
# Mouse library(TxDb.Mmusculus.UCSC.mm10.knownGene) library(org.Mm.eg.db) peak_anno_mm <- annotatePeak(peaks, TxDb = TxDb.Mmusculus.UCSC.mm10.knownGene, annoDb = 'org.Mm.eg.db') # Zebrafish library(TxDb.Drerio.UCSC.danRer11.refGene) library(org.Dr.eg.db)
Key Functions
| Function | Purpose |
|---|---|
| readPeakFile | Read peak file (BED, narrowPeak) |
| annotatePeak | Annotate peaks to genes |
| plotAnnoPie | Pie chart of annotations |
| plotAnnoBar | Bar plot of annotations |
| plotDistToTSS | Distance to TSS distribution |
| getPromoters | Get promoter regions |
| getTagMatrix | Coverage matrix around regions |
| tagHeatmap | Heatmap of signal |
| plotAvgProf | Average profile plot |
| seq2gene | Map peaks to all overlapping genes |
Annotation Categories
| Category | Description |
|---|---|
| Promoter | Within tssRegion of TSS |
| 5' UTR | 5' untranslated region |
| 3' UTR | 3' untranslated region |
| Exon | Coding exon |
| Intron | Intronic region |
| Downstream | Within 3kb downstream |
| Distal Intergenic | Beyond gene regions |
Related Skills
- peak-calling - Generate peak files with MACS3
- differential-binding - Find differential peaks
- pathway-analysis - Functional enrichment
- chipseq-visualization - Additional visualizations