How do I install the Bio Epitranscriptomics Modification Visualization skill?

Run npx skillmds@latest add fridrichmethod/bio-epitranscriptomics-modification-visualization in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Which AI agents work with Bio Epitranscriptomics Modification Visualization?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Who published Bio Epitranscriptomics Modification Visualization?

FridrichMethod (@fridrichmethod) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Bio Epitranscriptomics Modification Visualization by fridrichmethod

Name: Bio Epitranscriptomics Modification Visualization
Author: FridrichMethod

File contents

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

---name: bio-epitranscriptomics-modification-visualizationdescription: Create metagene plots and browser tracks for RNA modification data. Use when visualizing m6A distribution patterns around genomic features like stop codons.---# Modification Visualization## Metagene Plots with Guitar```rlibrary(Guitar)library(TxDb.Hsapiens.UCSC.hg38.knownGene)# Load m6A peakspeaks <- import('m6a_peaks.bed')# Create metagene plot# Shows distribution relative to transcript featuresGuitarPlot( peaks, txdb = TxDb.Hsapiens.UCSC.hg38.knownGene, saveToPDFprefix = 'm6a_metagene')```## Custom Metagene with deepTools```bash# Create bigWig from IP/Input ratiobamCompare -b1 IP.bam -b2 Input.bam \ --scaleFactors 1:1 \ --ratio log2 \ -o IP_over_Input.bw# Metagene around stop codonscomputeMatrix scale-regions \ -S IP_over_Input.bw \ -R genes.bed \ --regionBodyLength 2000 \ -a 500 -b 500 \ -o matrix.gzplotProfile -m matrix.gz -o metagene.pdf```## Browser Tracks```bash# Create normalized bigWig for genome browserbamCoverage -b IP.bam \ --normalizeUsing CPM \ -o IP_normalized.bw# Peak BED to bigBedbedToBigBed m6a_peaks.bed chrom.sizes m6a_peaks.bb```## Heatmaps```rlibrary(ComplexHeatmap)# m6A signal around peaksHeatmap( 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

Frequently asked questions

Bio Epitranscriptomics Modification Visualization