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Get Started Free →Create metagene plots and browser tracks for RNA modification data. Use when visualizing m6A distribution patterns around genomic features like stop codons.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -35% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -23% | 0% |
| case-14 | ✓→✓ | ＝ Same ✓ | 13% | 0% |
| case-03 | ✓→✓ | ＝ Same ✓ | -59% | 0% |
| case-06 | ✓→✓ | ＝ Same ✓ | -15% | 0% |
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rlibrary(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' )
bash# 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
bash# 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
rlibrary(ComplexHeatmap) # m6A signal around peaks Heatmap( signal_matrix, name = 'm6A signal', cluster_rows = TRUE, show_row_names = FALSE )
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