Bio Chipseq Peak Annotation

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

Annotation Categories

Related Skills

• peak-calling - Generate peak files with MACS3
• differential-binding - Find differential peaks
• pathway-analysis - Functional enrichment
• chipseq-visualization - Additional visualizations