bio-epitranscriptomics-modification-visualization

name	bio-epitranscriptomics-modification-visualization
description	Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot; MetaPlotR; deepTools computeMatrix scale-regions), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (log2 IP/input bigWig via deepTools bamCompare; pyGenomeTracks; Gviz; IGV/UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), feature-distribution stacked bars, and volcano/MA plots for differential modification. Establishes stop-codon enrichment in the metagene plot as the biological QC anchor for any MeRIP dataset (Dominissini 2012; Meyer 2012). Use when producing the canonical metagene plot with stop-codon enrichment as a QC anchor, building paired IP/input genome-browser tracks at single-locus resolution, plotting peak-centred heatmaps clustered by condition, summarising peak distribution across transcript features, generating DRACH motif logos as sanity checks, rendering volcano plots of differential m6A, or reproducing the stop-codon enrichment plot.
tool_type	mixed
primary_tool	Guitar

Version Compatibility

Reference examples tested with: Guitar 2.18+ (Bioconductor), MetaPlotR (GitHub, unversioned), deepTools 3.5+, ggcoverage 1.4+, pyGenomeTracks 3.8+, Gviz 1.46+, ComplexHeatmap 2.18+, ggseqlogo 0.2+, ggplot2 3.5+, rtracklayer 1.62+, GenomicFeatures 1.54+, BSgenome.Hsapiens.UCSC.hg38 1.4+, TxDb.Hsapiens.UCSC.hg38.knownGene 3.18+, ChIPseeker 1.38+.

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

• R: packageVersion('Guitar') then ?GuitarPlot to verify parameters
• CLI: <tool> --help; deepTools <tool> --help

If R throws unused argument or argument is missing, the API moved between Bioconductor minor releases. Guitar's older API used txdb= while newer uses txTxdb=; verify with ?GuitarPlot. deepTools bamCompare --operation log2 is the modern syntax (older --ratio log2 is being phased out).

RNA Modification Visualisation

"Make the canonical m6A metagene plot for my paper" -> Render the 5'UTR / CDS / 3'UTR transcript-feature metagene with stop-codon enrichment for visual confirmation of the canonical m6A topology (THE smoke test that the antibody-IP captured real m6A), the peak-feature-distribution stacked bar ("where do my peaks land?" Figure 1), peak-centred heatmaps clustered by condition, IP-over-Input paired browser tracks at specific loci, the DRACH sequence logo as a sanity check on antibody specificity, and the volcano / MA plots for differential modification. CRITICAL: the stop-codon enrichment plot is a biological-QC anchor — a MeRIP dataset that does NOT show enrichment at and around the stop codon indicates IP failure, wrong antibody, wrong protocol, or the assay captured a different modification (e.g., m1A is centred at TSS, not stop). Build this plot FIRST as a smoke test BEFORE any downstream visualisation.

• R: Guitar::GuitarPlot(peakBedFiles, txTxdb=...) -- canonical transcript-feature metagene (THE field-standard plot)
• CLI: deeptools computeMatrix scale-regions ... + plotProfile -- genome-coordinate metagene
• R: ComplexHeatmap::Heatmap() on peak-centred signal matrix -- peak-centred heatmap clustered by condition
• CLI: pyGenomeTracks --tracks tracks.ini --region chr:start-end -- multi-track browser plot
• R: ggcoverage::ggcoverage(data=track, mark.region=peaks) + geom_gene() -- ggplot2-based browser tracks
• R: ggseqlogo::ggseqlogo(seqs) -- DRACH sequence logo from peak-centre 5-mers
• R: ChIPseeker::annotatePeak() + ggplot2 stacked bar -- 5'UTR / CDS / 3'UTR feature distribution

The Single Most Important Modern Insight -- Stop-codon enrichment in the metagene plot is the biological QC anchor — a MeRIP dataset without it is suspect

Dominissini 2012 Nature 485:201 and Meyer 2012 Cell 149:1635 — concurrent papers from different labs using different protocols (MeRIP-seq and m6A-seq, respectively) — both independently showed m6A enrichment at and around the stop codon (3'UTR-proximal end of the CDS). This is the most robust biological signal in MeRIP-seq, reproduced across cell types, conditions, and decades. A metagene plot from a MeRIP library that does NOT show stop-codon enrichment indicates: (1) IP failure (antibody did not bind), (2) wrong antibody, (3) wrong protocol (e.g., the assay actually captured a different modification — m1A is centred at TSS, not stop), or (4) sample-RNA degradation. Build the Guitar metagene plot FIRST after peak calling, BEFORE any downstream visualisation, as a smoke test. The corollary: the 5'UTR / CDS / 3'UTR feature-distribution stacked bar (the "where do m6A peaks land?" Figure 1) should always be PAIRED with the metagene plot, because the stacked bar can look right (peaks land in 3'UTR / stop area) while the metagene is off (peak DENSITY not concentrated at the codon itself), or vice versa. The two plots are complementary biology-QC anchors, not redundant.

Algorithmic Taxonomy

Tool / plot	Mechanism	Inputs	Output	Strength	Fails when
Guitar GuitarPlot (Cui 2016 Biomed Res Int 2016:8367534)	Per-peak distance computation relative to TSS / start / stop / TES; feature-scaled rendering	BED + TxDb	PDF + per-feature density	THE field-standard m6A metagene; transcript-feature-aware	Older / newer API uses txdb= vs txTxdb= argument
MetaPlotR (Olarerin-George 2017 Bioinformatics 33:1563)	Alternative metagene approach with different segment-rescaling	BED + GTF	PDF	Alternative segment rescaling philosophy	GitHub-only; less actively maintained
deepTools computeMatrix + plotProfile (Ramírez 2016 NAR 44:W160)	Genome-coordinate signal aggregation over scaled gene regions	bigWig + BED	PDF + matrix	Fast; flexible region/anchor choice; well-tested ChIP-seq lineage	Not transcript-feature-aware; misses 5'UTR / CDS / 3'UTR distinction
deepTools plotHeatmap	Same matrix as plotProfile; rendered as heatmap with row clustering	bigWig matrix	PDF	Standard peak-centred heatmap	Single-condition view (use ComplexHeatmap for multi-condition clustering)
ComplexHeatmap (Gu 2016 Bioinformatics 32:2847)	General-purpose heatmap with multi-dimensional clustering	numeric matrix	PDF / interactive	Multi-condition cluster + annotation; publication-quality	Requires pre-computed signal matrix
pyGenomeTracks (Lopez-Delisle 2021 Bioinformatics 37:422)	Config-file (INI) driven multi-track browser plot	bigWig / bed / GTF + INI config	PDF / PNG	Reproducible browser figures via config; multi-track stacking	INI config syntax error-prone for new users
ggcoverage (Song & Wang 2023 BMC Bioinformatics 24:309)	ggplot2-native track plotting	bigWig / BAM + GTF	ggplot2 object	ggplot2 syntax; combinable with annotation layers	Newer tool; smaller user base than Gviz
Gviz (Hahne & Ivanek 2016 Methods Mol Biol 1418:335)	R/Bioconductor general genome track

Decision Tree by Scenario

Scenario	Recommended	Why wrong choices fail
Canonical Figure 1 metagene plot for m6A paper	Guitar GuitarPlot with TxDb -- transcript-feature scaled	deepTools computeMatrix is genome-coordinate only; misses 5'UTR / CDS / 3'UTR distinction
QC: does my MeRIP show stop-codon enrichment?	Guitar GuitarPlot FIRST -- the canonical biological-QC anchor	Skipping this and rushing to peak counting is the most common QC failure
Browser-track Figure for a specific locus	pyGenomeTracks (config-file driven; reproducible) OR ggcoverage (ggplot2-native)	IGV is interactive but not reproducible for figures
Multi-condition peak-centred heatmap with clustering	ComplexHeatmap; row-cluster k-means; annotate by condition	deepTools plotHeatmap is single-condition view
DRACH sequence logo from peak centres	ggseqlogo on resized peak ranges (width=5, fix='center')	MEME-ChIP heavier than needed for visualisation only
Differential m6A volcano plot	ggplot2 native (see m6a-differential) -- modification-visualization should NOT duplicate	Duplicating volcano recipe across skills inflates content
Genome-coordinate metagene over custom features	deepTools computeMatrix scale-regions; plotProfile	Guitar restricted to transcript features; doesn't scale to arbitrary BED
Cross-sample paired IP/input track display	bamCompare -> log2 bigWig; pyGenomeTracks multi-track	Single bigWig hides the IP/input pairing structure
5'UTR / CDS / 3'UTR stacked bar	ChIPseeker annotatePeak -> ggplot2 stacked bar	Manual annotation error-prone; ChIPseeker handles edge cases
Reproducing Dominissini 2012 / Meyer 2012 stop-codon plot	Guitar GuitarPlot; the canonical recipe	Other tools produce similar but not identical plots

Methodology evolves; before high-stakes figure generation, web-search "Guitar Bioconductor release notes" and "pyGenomeTracks vs ggcoverage" for current best practice.

Guitar Transcript-Feature Metagene (THE Canonical Plot)

Goal: Render the canonical m6A metagene plot showing peak density along scaled transcript features (5'UTR, CDS, 3'UTR), with the stop-codon-proximal enrichment that is the field's biological QC anchor.

Approach: Load called peaks from BED; pass to GuitarPlot with the matched TxDb; export per-feature density and the PDF.

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

txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene

# Older Guitar versions: argument is `txdb=`; newer: `txTxdb=`. Verify with ?GuitarPlot.
GuitarPlot(
    txTxdb           = txdb,
    stBedFiles       = list('exomepeak2_output/m6a_run1/peaks.bed'),
    miscOutFilePrefix = 'figures/m6a_metagene',
    enableCI         = FALSE,
    saveToPDFprefix  = 'figures/m6a_metagene'
)

If txTxdb= is rejected, fall back to txdb= and consult ?GuitarPlot. The expected output: stop-codon-proximal enrichment, with peak density rising toward and peaking near the stop codon in the 3'UTR. If the plot does NOT show this pattern, do not proceed to downstream visualisation — investigate IP / antibody / protocol failure in merip-preprocessing.

deepTools Genome-Coordinate Metagene

Goal: Render a genome-coordinate metagene over scaled gene regions using deepTools, useful when transcript-feature scaling is not needed or when the regions of interest are not standard genes (custom BED).

Approach: Build a signal matrix with computeMatrix scale-regions from a bigWig (typically IP-over-Input log2 from merip-preprocessing); render as profile plot + heatmap.

mkdir -p figures

computeMatrix scale-regions \
    --regionsFileName refs/protein_coding.bed \
    --scoreFileName tracks/IP_rep1_over_Input_rep1.bw \
    --regionBodyLength 2000 \
    --upstream 500 \
    --downstream 500 \
    --skipZeros \
    --numberOfProcessors 8 \
    --outFileName figures/m6a_matrix.gz

plotProfile \
    --matrixFile figures/m6a_matrix.gz \
    --outFileName figures/m6a_profile.pdf \
    --plotTitle 'm6A IP over Input metagene' \
    --plotType lines

plotHeatmap \
    --matrixFile figures/m6a_matrix.gz \
    --outFileName figures/m6a_heatmap.pdf \
    --colorMap RdBu_r \
    --plotTitle 'm6A IP over Input heatmap'

scale-regions is the right mode for gene-body metagene (5'-end-to-3'-end scaled to common length); reference-point is for peak-centred plots. For peak-centred, see the next section.

Peak-Centred Heatmap

Goal: Render a peak-centred heatmap of IP-over-Input signal at +/-window around each peak, clustered by condition or by signal pattern, for cross-condition comparison.

Approach: Use deepTools computeMatrix reference-point --referencePoint center for the matrix; ComplexHeatmap for the clustered render.

computeMatrix reference-point \
    --regionsFileName exomepeak2_output/m6a_run1/peaks.bed \
    --scoreFileName tracks/IP_rep1_over_Input_rep1.bw tracks/IP_rep2_over_Input_rep2.bw tracks/IP_rep3_over_Input_rep3.bw \
    --referencePoint center \
    --upstream 500 \
    --downstream 500 \
    --binSize 25 \
    --skipZeros \
    --numberOfProcessors 8 \
    --outFileName figures/peak_centred_matrix.gz \
    --outFileNameMatrix figures/peak_centred_matrix.tab

plotHeatmap \
    --matrixFile figures/peak_centred_matrix.gz \
    --outFileName figures/peak_centred_heatmap.pdf \
    --kmeans 3 \
    --colorMap viridis \
    --plotTitle 'm6A signal centred at peaks'

For multi-condition heatmap with annotations, parse the matrix into R and use ComplexHeatmap:

library(ComplexHeatmap)
library(circlize)

# deepTools --outFileNameMatrix has a 3-line JSON-style header before per-bin numeric columns.
raw <- read.delim('figures/peak_centred_matrix.tab', skip=3, header=FALSE)
mat <- as.matrix(raw[, -(1:6)])

col_fun <- colorRamp2(c(-2, 0, 2), c('blue', 'white', 'red'))

Heatmap(
    mat,
    name              = 'log2 IP / Input',
    col               = col_fun,
    cluster_columns   = 
    cluster_rows       
    row_km             
    show_row_names     
    show_column_names  
    column_title       

IP-vs-Input Paired Browser Tracks via pyGenomeTracks

Goal: Render publication-quality genome-browser tracks at a specific locus showing paired IP / Input bigWig tracks, the IP/input log2 ratio, peak calls, and gene annotation; reproducible via INI config.

Approach: Build a tracks.ini config file listing each track type (bigwig, bed, gtf); invoke pyGenomeTracks --tracks tracks.ini --region chr:start-end.

[x-axis]
where = top
fontsize = 12

[IP rep1]
file = tracks/IP_rep1.bw
title = IP rep1
color = #d62728
height = 2
min_value = 0

[Input rep1]
file = tracks/Input_rep1.bw
title = Input rep1
color = #1f77b4
height = 2
min_value = 0

[IP / Input log2]
file = tracks/IP_rep1_over_Input_rep1.bw
title = log2 IP / Input
color = #2ca02c
height = 2

[spacer]

[m6A peaks]
file = exomepeak2_output/m6a_run1/peaks.bed
title = m6A peaks (exomePeak2)
color = #ff7f0e
height = 1
labels = false

[genes]
file = refs/annotation.gtf
title = GENCODE genes
height = 2
prefered_name = gene_name
merge_transcripts = true

pyGenomeTracks \
    --tracks tracks.ini \
    --region chr19:54,792,000-54,799,000 \
    --outFileName figures/browser_metti3_locus.pdf \
    --width 14 \
    --plotWidth 12

ggcoverage ggplot2-Native Browser Track

Goal: Render genome-browser tracks in ggplot2 syntax for combining with other ggplot2 layers (annotations, peak highlights, custom theming).

Approach: LoadTrackFile() parses a bigWig / bigBed / BAM input into the dataframe ggcoverage expects; chain ggcoverage() with geom_gene() for transcript annotation; mark.region requires columns start, end, and label.

library(ggcoverage)
library(rtracklayer)

peaks <- as.data.frame(import('exomepeak2_output/m6a_run1/peaks.bed'))

track.df <- LoadTrackFile(
    track.file = 'tracks/IP_rep1_over_Input_rep1.bw',
    format     = 'bw',
    region     = 'chr19:54792000-54799000'
)

mark.df <- data.frame(
    start = peaks$start,
    end   = peaks$end,
    label = peaks$name
)

ggcoverage(
    data        = track.df,
    color       = 'auto',
    mark.region = mark.df
) +
    geom_gene(gtf.file = 'refs/annotation.gtf') +
    ggplot2::theme_classic()

DRACH Sequence Logo

Goal: Render a sequence logo of peak-centre 5-mers as a sanity check that the called peak set is enriched for the DRACH consensus motif.

Approach: Resize peak GRanges to fixed 5-nt centred windows; extract genomic sequences; pass to ggseqlogo with method='probability'.

library(Biostrings)
library(BSgenome.Hsapiens.UCSC.hg38)
library(ggseqlogo)
library(rtracklayer)

peaks <- import('exomepeak2_output/m6a_run1/peaks.bed')

peak_centres <- resize(peaks, width=5, fix='center')

genome <- BSgenome.Hsapiens.UCSC.hg38
seqs <- as.character(getSeq(genome, peak_centres))

ggseqlogo(seqs, method='probability') +
    ggplot2::labs(title='Peak-centre 5-mer (DRACH consensus expected)',
                  subtitle='Sanity check on antibody specificity — NOT a per-peak filter')

The expected output: a logo showing approximately D-R-A-C-H consensus (D=A/G/U, R=A/G, A=methylated, C, H=A/C/U) with A clearly dominating position 3. If the logo does NOT show DRACH-like enrichment, the IP failed OR the wrong antibody was used.

5'UTR / CDS / 3'UTR Stacked Bar

Goal: Render the Figure 1 "where do my peaks land?" stacked bar showing the fraction of peaks in 5'UTR vs CDS vs 3'UTR vs intron, paired with the metagene to confirm canonical m6A topology.

Approach: Use ChIPseeker annotatePeak() with a matched TxDb; aggregate to per-feature counts; render as ggplot2 stacked bar.

library(ChIPseeker)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(rtracklayer)
library(ggplot2)
library(dplyr)

txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
peaks <- import('exomepeak2_output/m6a_run1/peaks.bed')

peak_anno <- annotatePeak(peaks, TxDb=txdb, level='transcript', verbose=FALSE)
anno_df <- as.data.frame(peak_anno@anno)

anno_df$feature <- gsub(' \\(.*\\)', '', anno_df$annotation)

feature_counts <- anno_df %>%
    group_by(feature) %>%
    summarise(n=n()) %>%
    mutate(fraction=n/n 
    arrangedescfraction

ggplotfeature_counts aesx yfraction fillfeature 
    geom_colwidth 
    scale_y_continuouslabelsscalespercent_format 
    labsx y title 
    theme_minimal

Per-Method Failure Modes

Metagene without stop-codon enrichment

Trigger: Guitar GuitarPlot of called peaks does NOT show enrichment at and around the stop codon.

Mechanism: Canonical m6A topology (Dominissini 2012 / Meyer 2012) shows stop-codon-proximal enrichment. Absence indicates (1) IP failure, (2) wrong antibody, (3) wrong protocol, (4) sample-RNA degradation, OR (5) the assay captured a different modification (m1A is TSS-centred; m5C distribution differs).

Symptom: Metagene is flat OR peaks at TSS (m1A signature) OR peaks in introns (unusual).

Fix: Do NOT proceed to downstream analysis. Diagnose at the merip-preprocessing layer: plotFingerprint, per-transcript IP/input distribution, antibody-lot QC. Re-do the IP if necessary. The metagene plot is the biological QC anchor; without it, all downstream interpretation is suspect.

DRACH logo not enriched

Trigger: ggseqlogo of peak-centre 5-mers shows no consensus, OR shows a non-DRACH-like motif.

Mechanism: Antibody specificity failure OR wrong protocol. Anti-m6A antibodies have ~70% DRACH-context enrichment on real m6A peaks; a failed IP captures random sequences with no consensus.

Fix: Re-inspect IP enrichment in merip-preprocessing. If IP is clean but DRACH logo is absent, the assay may have captured a different modification — investigate before claiming m6A.

Guitar txdb= vs txTxdb= argument confusion

Trigger: GuitarPlot(stBedFiles=..., txdb=txdb) rejected with "unused argument" error.

Mechanism: Guitar changed the argument name between Bioconductor releases — txdb= (older) vs txTxdb= (newer).

Fix: Try alternative; consult ?GuitarPlot for installed version. Pin Guitar version in reproducible analyses.

deepTools genome-coordinate metagene used where transcript-feature is needed

Trigger: computeMatrix scale-regions over a BED of genes used to show "5'UTR / CDS / 3'UTR enrichment".

Mechanism: deepTools scales by genomic length, NOT by transcript-feature length. A gene with long 5'UTR and short CDS will scale 5'UTR more than CDS; the metagene loses 5'UTR / CDS / 3'UTR semantics.

Fix: Use Guitar (transcript-feature-aware) for 5'UTR / CDS / 3'UTR semantics. Use deepTools for genome-coordinate metagene over arbitrary BED.

IGV-only browser figure in published paper

Trigger: Figure 4 of paper shows an IGV screenshot of one locus; not reproducible from code.

Mechanism: IGV is interactive; the figure cannot be re-generated from a config file. Reviewers cannot reproduce the figure if track files change.

Fix: Use pyGenomeTracks (INI config) or ggcoverage (ggplot2 script) for figures intended for publication. IGV is for ad-hoc inspection only.

bamCompare --ratio log2 deprecation

Trigger: Older deepTools syntax --ratio log2 used; newer requires --operation log2.

Fix: Switch to --operation log2. Both currently work but --ratio is being phased out.

ggseqlogo "sequences must be equal length"

Trigger: Mixed-length peak sequences passed to ggseqlogo.

Mechanism: ggseqlogo requires equal-length input strings.

Fix: Resize peak ranges to fixed width before extraction: resize(peaks, width=5, fix='center').

pyGenomeTracks INI typo

Trigger: Track section header missing brackets, OR file = instead of file= (whitespace inconsistency).

Fix: Use the documented INI syntax precisely; bracketed section headers; consistent whitespace around =. Validate with pyGenomeTracks --tracks tracks.ini --region 'chr:start-end' --outFileName out.pdf and iterate on errors.

Reconciliation: When Plots Disagree

Pattern	Likely cause	Action
Metagene shows stop-codon enrichment but stacked bar has many 5'UTR peaks	5'UTR peaks are present but density is concentrated at stop codon	Both plots are correct; report together
Guitar metagene and deepTools metagene look very different	Guitar transcript-feature-scaled vs deepTools genome-scaled	Both correct; Guitar for 5'UTR / CDS / 3'UTR semantics; deepTools for genome coordinates
Peak-centred heatmap shows two clusters; condition annotation crosses cluster boundary	Biological signal not condition-aligned; OR clustering driven by per-peak coverage variance	Inspect per-cluster fold-change; consider removing low-coverage peaks before re-clustering
pyGenomeTracks shows IP track higher than Input but bamCompare bigWig shows log2 near zero	Track signal magnitudes are CPM-normalised; absolute counts and log2 ratios are different summaries	Report log2 ratio as the primary; per-track CPM as supplement
DRACH logo enriched but stacked bar shows mostly intronic peaks	Intronic m6A (Louloupi 2018 nascent transcripts)	Genuine biology; report intronic vs exonic separately; consider library prep (poly-A vs ribo-depleted)
Volcano plot symmetric but most differential peaks in 3'UTR	Differential is feature-restricted	Cross-check with per-feature differential testing; biological interpretation

Quantitative Thresholds

Quantity	Threshold	Source / rationale
Guitar metagene -- expected pattern	Stop-codon-proximal enrichment, 3'UTR > CDS > 5'UTR density	Dominissini 2012 Nature 485:201; Meyer 2012 Cell 149:1635
deepTools metagene scaled-region length	2000 bp body + 500 bp flanks	Convention; covers typical mammalian gene span
Peak-centred heatmap window	+/-500 bp around peak centre	Standard convention; covers MeRIP fragment width
Peak-centred heatmap k-means clusters	3-5 typical	Cluster count informed by condition count + signal heterogeneity
ggseqlogo expected DRACH pattern	A dominant at position 3 (the methylated A); C dominant at position 4	DRACH consensus
pyGenomeTracks figure width	10-14 inches	Standard publication width
Browser-track region width	5-50 kb	Locus-context dependent
5'UTR / CDS / 3'UTR / intron+other expected distribution	~10% / ~30% / ~50% / ~10% for canonical m6A	Per published m6A atlases
DRACH logo "method" parameter	'probability' for visual; 'bits' for information-theoretic	ggseqlogo convention
Per-feature stacked bar minimum peaks	>=100	Below this, fractions are noisy
Cross-replicate metagene divergence	Should be near-identical within condition	If replicates' metagenes diverge, failed-IP suspect in merip-preprocessing

Common Errors

Error / symptom	Cause	Solution
Guitar txTxdb rejected	Older version uses txdb	Switch argument name; consult ?GuitarPlot
Guitar PDF blank	BED file empty OR chromosome mismatch with TxDb	Verify peak BED has peaks; reconcile chromosome naming
deepTools computeMatrix errors with "no regions"	BED file empty OR file path wrong	Verify BED is non-empty and path correct
pyGenomeTracks INI parse error	Bracket / whitespace inconsistency	Match documented INI syntax exactly
ggcoverage region not displayed	Region beyond bigWig coverage; OR chromosome naming mismatch	Verify bigWig contains region; reconcile chr1 vs 1
ggseqlogo throws "equal length" error	Mixed-length sequences	resize(peaks, width=5, fix='center') before extracting
ComplexHeatmap clustering hangs	Very large peak set (>100k)	Subset to top N peaks; or use deepTools plotHeatmap k-means
bamCompare --ratio log2 deprecation warning	Newer syntax	Switch to --operation log2
IGV screenshot not reproducible	Interactive tool	Switch to pyGenomeTracks / ggcoverage
ggseqlogo logo blank	Empty seqs input OR all sequences are gaps / Ns	Verify peak BED resolves to valid genomic sequences via getSeq()
Stacked bar shows >100%	Peak annotation overlap counted multiple times	Use annotatePeak with explicit hierarchy
ComplexHeatmap colour scale wrong	colorRamp2 breaks at outliers	Set scale based on robust quantiles (quantile(x, 0.05), 0, quantile(x, 0.95))

Anticipated Reviewer Pushback

Pushback	Response
"Does the MeRIP show the canonical stop-codon enrichment?"	Yes — Guitar metagene plot shows expected stop-codon-proximal enrichment; cited Dominissini 2012 / Meyer 2012
"Why Guitar and not deepTools?"	Guitar is transcript-feature-scaled (5'UTR / CDS / 3'UTR semantics); deepTools is genome-coordinate; both reported when needed
"Is the DRACH motif enriched?"	ggseqlogo of peak-centre 5-mers; OR HOMER findMotifsGenome.pl E-value reported
"Is the browser figure reproducible?"	Yes — pyGenomeTracks INI config OR ggcoverage R script; not IGV screenshot
"How were peaks annotated to features?"	ChIPseeker annotatePeak with matched TxDb; hierarchy explicit
"Why these specific clusters in the heatmap?"	k-means with k=3 chosen via elbow / silhouette; clusters reflect signal heterogeneity
"Does the metagene differ between conditions?"	Per-condition metagenes plotted alongside; differences quantified at the feature level
"Why is the colour scheme red-blue?"	Standard convention for log2 ratios (red = up, blue = down); colour-blind-safe palette via viridis available
"Was a cross-check with published m6A-Atlas peaks done?"	Common-core overlap reported; cited m6A-Atlas v2
"Are the browser track signal magnitudes comparable across samples?"	bigWig CPM-normalised in merip-preprocessing; documented

References

• Dominissini D, Moshitch-Moshkovitz S, Schwartz S et al (2012) Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485(7397):201-206. doi:10.1038/nature11112
• Meyer KD, Saletore Y, Zumbo P, Elemento O, Mason CE, Jaffrey SR (2012) Comprehensive analysis of mRNA methylation reveals enrichment in 3' UTRs and near stop codons. Cell 149(7):1635-1646. doi:10.1016/j.cell.2012.05.003
• Cui X, Wei Z, Zhang L et al (2016) Guitar: an R/Bioconductor package for gene annotation guided transcriptomic analysis of RNA-related genomic features. Biomed Res Int 2016:8367534. doi:10.1155/2016/8367534
• Olarerin-George AO, Jaffrey SR (2017) MetaPlotR: a Perl/R pipeline for plotting metagenes of nucleotide modifications and other transcriptomic sites. Bioinformatics 33(10):1563-1564. doi:10.1093/bioinformatics/btx002
• Ramírez F, Ryan DP, Grüning B et al (2016) deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res 44(W1):W160-W165. doi:10.1093/nar/gkw257
• Gu Z, Eils R, Schlesner M (2016) Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32(18):2847-2849. doi:10.1093/bioinformatics/btw313
• Lopez-Delisle L, Rabbani L, Wolff J et al (2021) pyGenomeTracks: reproducible plots for multivariate genomic datasets. Bioinformatics 37(3):422-423. doi:10.1093/bioinformatics/btaa692
• Song Y, Wang J (2023) ggcoverage: an R package to visualize and annotate genome coverage for various NGS data. BMC Bioinformatics 24(1):309. doi:10.1186/s12859-023-05438-2
• Robinson JT, Thorvaldsdóttir H, Winckler W et al (2011) Integrative genomics viewer. Nat Biotechnol 29(1):24-26. doi:10.1038/nbt.1754
• Wagih O (2017) ggseqlogo: a versatile R package for drawing sequence logos. Bioinformatics 33(22):3645-3647. doi:10.1093/bioinformatics/btx469
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Related Skills

• merip-preprocessing - Generates the bigWig tracks (bamCompare log2 IP/input) used here
• m6a-peak-calling - Generates the peak BED used for metagene + stacked bar + heatmap
• m6a-differential - Differential results visualised via volcano + MA (those plots live in m6a-differential, not duplicated here)
• m6anet-analysis - Per-site DRS modification calls; visualisation analogous via metagene
• data-visualization/ggplot2-fundamentals - General ggplot2 grammar
• data-visualization/multipanel-figures - Combining metagene + heatmap + volcano into figures
• data-visualization/heatmaps-clustering - General heatmap clustering patterns
• data-visualization/volcano-and-ma-plots - General volcano / MA recipes (modification-specific volcano lives in m6a-differential)
• data-visualization/genome-tracks - General genome-track rendering (this skill adds the IP/input pairing specifics)
• data-visualization/sequence-logos - General sequence-logo plotting (this skill adds DRACH-specific context)
• chip-seq/chipseq-visualization - Closest sibling for browser-track + peak-centred heatmap patterns
• pathway-analysis/enrichment-visualization - For visualising downstream pathway results