ReddRadar
Agent skill
bio-epitranscriptomics-m6a-peak-calling
Install this agent skill to your Project
npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-epitranscriptomics-m6a-peak-calling
SKILL.md
name: bio-epitranscriptomics-m6a-peak-calling description: Call m6A peaks from MeRIP-seq IP vs input comparisons. Use when identifying m6A modification sites from methylated RNA immunoprecipitation data. tool_type: mixed primary_tool: exomePeak2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
m6A Peak Calling
exomePeak2 (Recommended)
library(exomePeak2)
# Peak calling with biological replicates
result <- exomePeak2(
bam_ip = c('IP_rep1.bam', 'IP_rep2.bam'),
bam_input = c('Input_rep1.bam', 'Input_rep2.bam'),
gff = 'genes.gtf',
genome = 'hg38',
paired_end = TRUE
)
# Export peaks
exportResults(result, format = 'BED')
MACS3 Alternative
# Call peaks treating input as control
macs3 callpeak \
-t IP_rep1.bam IP_rep2.bam \
-c Input_rep1.bam Input_rep2.bam \
-f BAMPE \
-g hs \
-n m6a_peaks \
--nomodel \
--extsize 150 \
-q 0.05
MeTPeak
library(MeTPeak)
# GTF-aware peak calling
metpeak(
IP_BAM = c('IP_rep1.bam', 'IP_rep2.bam'),
INPUT_BAM = c('Input_rep1.bam', 'Input_rep2.bam'),
GENE_ANNO_GTF = 'genes.gtf',
OUTPUT_DIR = 'metpeak_output'
)
Peak Filtering
# Filter by fold enrichment and q-value
# FC > 2, q < 0.05 typical thresholds
awk '$7 > 2 && $9 < 0.05' peaks.xls > filtered_peaks.bed
Related Skills
- merip-preprocessing - Prepare data for peak calling
- m6a-differential - Compare peaks between conditions
- chip-seq/peak-calling - Similar concepts
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