Version Compatibility

Reference examples tested with: deepTools 3.5+

Before using code patterns, verify installed versions match. If versions differ:

• R:

  packageVersion('<pkg>')

  then

  ?function_name

  to verify parameters
• CLI:

  <tool> --version

  then

  <tool> --help

  to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Modification Visualization

"Visualize m6A distribution around stop codons" → Create metagene plots and genome browser tracks showing RNA modification patterns relative to transcript landmarks (5'UTR, CDS, 3'UTR, stop codon).

• R:

  Guitar::GuitarPlot()

  for metagene distribution plots
• CLI:

  deeptools computeMatrix

  →

  plotHeatmap

  for modification heatmaps

Metagene Plots with Guitar

library(Guitar)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)

# Load m6A peaks
peaks <- import('m6a_peaks.bed')

# Create metagene plot
# Shows distribution relative to transcript features
GuitarPlot(
    peaks,
    txdb = TxDb.Hsapiens.UCSC.hg38.knownGene,
    saveToPDFprefix = 'm6a_metagene'
)

Custom Metagene with deepTools

Goal: Create a metagene profile showing m6A enrichment distribution relative to gene body landmarks (TSS, TES).

Approach: Compute the log2 IP/input ratio as a bigWig track with bamCompare, then build a signal matrix over scaled gene regions with computeMatrix and render as a profile plot.

# Create bigWig from IP/Input ratio
bamCompare -b1 IP.bam -b2 Input.bam \
    --scaleFactors 1:1 \
    --ratio log2 \
    -o IP_over_Input.bw

# Metagene around stop codons
computeMatrix scale-regions \
    -S IP_over_Input.bw \
    -R genes.bed \
    --regionBodyLength 2000 \
    -a 500 -b 500 \
    -o matrix.gz

plotProfile -m matrix.gz -o metagene.pdf

Browser Tracks

# Create normalized bigWig for genome browser
bamCoverage -b IP.bam \
    --normalizeUsing CPM \
    -o IP_normalized.bw

# Peak BED to bigBed
bedToBigBed m6a_peaks.bed chrom.sizes m6a_peaks.bb

Heatmaps

library(ComplexHeatmap)

# m6A signal around peaks
Heatmap(
    signal_matrix,
    name = 'm6A signal',
    cluster_rows = TRUE,
    show_row_names = FALSE
)

Related Skills

• epitranscriptomics/m6a-peak-calling - Generate peaks for visualization
• data-visualization/genome-tracks - IGV, UCSC integration
• chip-seq/chipseq-visualization - Similar techniques