bio-epitranscriptomics-modification-visualization
Create metagene plots and browser tracks for RNA modification data. Use when visualizing m6A distribution patterns around genomic features like stop codons.
Best use case
bio-epitranscriptomics-modification-visualization is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Create metagene plots and browser tracks for RNA modification data. Use when visualizing m6A distribution patterns around genomic features like stop codons.
Teams using bio-epitranscriptomics-modification-visualization should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/bio-epitranscriptomics-modification-visualization/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How bio-epitranscriptomics-modification-visualization Compares
| Feature / Agent | bio-epitranscriptomics-modification-visualization | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Create metagene plots and browser tracks for RNA modification data. Use when visualizing m6A distribution patterns around genomic features like stop codons.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Modification Visualization
## Metagene Plots with Guitar
```r
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
```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
```
## Browser Tracks
```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
```
## Heatmaps
```r
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
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