Skip to content
Data
Skill

/bio-epitranscriptomics-m6a-differential

<!--

From plugin
openclaw-medical-skills
2.9k200 skills
Install
$ npx -y skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-epitranscriptomics-m6a-differential --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/bio-epitranscriptomics-m6a-differential

Context preview

The summary Claude sees to decide when to auto-load this skill.

<!--

SKILL.md

bio-epitranscriptomics-m6a-differential.SKILL.md

<!--

COPYRIGHT NOTICE

This file is part of the "Universal Biomedical Skills" project.

Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>

All Rights Reserved.

#

This code is proprietary and confidential.

Unauthorized copying of this file, via any medium is strictly prohibited.

#

Provenance: Authenticated by MD BABU MIA

-->

--- name: bio-epitranscriptomics-m6a-differential description: Identify differential m6A methylation between conditions from MeRIP-seq. Use when comparing epitranscriptomic changes between treatment groups or cell states. tool_type: r primary_tool: exomePeak2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

  • read_file
  • run_shell_command

---

Differential m6A Analysis

exomePeak2 Differential Analysis

library(exomePeak2)

# Define sample design
# condition: factor for comparison
design <- data.frame(
    condition = factor(c('ctrl', 'ctrl', 'treat', 'treat'))
)

# Differential peak calling
result <- exomePeak2(
    bam_ip = c('ctrl_IP1.bam', 'ctrl_IP2.bam', 'treat_IP1.bam', 'treat_IP2.bam'),
    bam_input = c('ctrl_Input1.bam', 'ctrl_Input2.bam', 'treat_Input1.bam', 'treat_Input2.bam'),
    gff = 'genes.gtf',
    genome = 'hg38',
    experiment_design = design
)

# Get differential sites
diff_sites <- results(result, contrast = c('condition', 'treat', 'ctrl'))

QNB for Differential Methylation

library(QNB)

# Requires count matrices from peak regions
# IP and input counts per sample
qnb_result <- qnbtest(
    IP_count_matrix,
    Input_count_matrix,
    group = c(1, 1, 2, 2)  # 1=ctrl, 2=treat
)

# Filter significant
# padj < 0.05, |log2FC| > 1
sig <- qnb_result[qnb_result$padj < 0.05 & abs(qnb_result$log2FC) > 1, ]

Visualization

library(ggplot2)

# Volcano plot
ggplot(diff_sites, aes(x = log2FoldChange, y = -log10(padj))) +
    geom_point(aes(color = padj < 0.05 & abs(log2FoldChange) > 1)) +
    geom_hline(yintercept = -log10(0.05), linetype = 'dashed') +
    geom_vline(xintercept = c(-1, 1), linetype = 'dashed')

Related Skills

  • m6a-peak-calling - Identify peaks first
  • differential-expression/de-results - Similar statistical concepts
  • modification-visualization - Plot differential sites

<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->

Read more
Ships withopenclaw-medical-skills

The largest open-source medical AI skill library for OpenClaw.

Get the whole plugin
Stats
2,921
Stars
410
Forks
Active
Maintenance
Python
Language
20d ago
Last commit
5mo ago
Created

Repo: FreedomIntelligence/OpenClaw-Medical-Skills