ReddRadar
Agent skill
bio-chipseq-peak-annotation
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.
Install this agent skill to your Project
npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-chipseq-peak-annotation
SKILL.md
Version Compatibility
Reference examples tested with: MACS3 3.0+, clusterProfiler 4.10+
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>')then?function_nameto verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Peak Annotation with ChIPseeker
"Annotate my ChIP-seq peaks to genes" → Assign peaks to genomic features (promoter, exon, intron, intergenic), find nearest genes, and calculate TSS distances.
- R:
ChIPseeker::annotatePeak(peaks, TxDb=txdb)
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
Goal: Assign each ChIP-seq peak to its nearest gene and genomic feature category.
Approach: Use annotatePeak with a TxDb annotation database to classify peaks as promoter, exon, intron, or intergenic and retrieve the nearest gene symbol.
# 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
Goal: Compare genomic feature distributions across multiple ChIP-seq experiments (e.g., different histone marks).
Approach: Read and annotate each peak file separately, then use plotAnnoBar and plotDistToTSS on the annotation list for side-by-side comparison.
# 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)$SYMBOL)
vennplot(genes_list)
Coverage Plot
# Plot peak coverage around TSS
covplot(peaks, weightCol = 'V5') # V5 is score column in narrowPeak
Profile Heatmap Around TSS
Goal: Visualize the distribution of ChIP-seq signal around transcription start sites.
Approach: Extract promoter regions from the TxDb, build a tag matrix of signal at those regions, and plot as a heatmap or average profile.
# Get promoter coordinates
promoter <- getPromoters(TxDb = txdb, upstream = 3000, downstream = 3000)
# Get tag matrix
tagMatrix <- getTagMatrix(peaks, windows = promoter)
# Plot heatmap
tagHeatmap(tagMatrix, xlim = c(-3000, 3000), color = 'red')
# Average profile
plotAvgProf(tagMatrix, xlim = c(-3000, 3000), xlab = 'Distance from TSS')
Functional Enrichment of Peak Genes
Goal: Determine which biological processes are enriched among genes with ChIP-seq peaks in their promoters.
Approach: Extract Entrez IDs from annotated peaks and run GO enrichment analysis with clusterProfiler.
library(clusterProfiler)
# Get genes from peaks
genes <- unique(anno_df$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
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