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name: bio-epitranscriptomics-modification-visualization description: Create metagene plots and browser tracks for RNA modification data. Use when visualizing m6A distribution patterns around genomic features like stop codons. tool_type: r primary_tool: Guitar measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

  • read_file
  • run_shell_command

Modification Visualization

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

# 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