bio-epitranscriptomics-m6a-peak-calling
$
npx mdskill add GPTomics/bioSkills/bio-epitranscriptomics-m6a-peak-callingIdentify m6A peaks from MeRIP-seq IP versus input data
- Detect methylated RNA sites by comparing immunoprecipitated and control samples
- Depends on exomePeak2 R package or MACS3 CLI peak calling tools
- Uses statistical enrichment testing with GC-bias correction for significance
- Returns significant genomic regions matching user-specified BAM file inputs
SKILL.md
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---
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
---
## Version Compatibility
Reference examples tested with: MACS3 3.0+
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# m6A Peak Calling
**"Call m6A peaks from my MeRIP-seq data"** → Identify m6A-modified RNA regions by comparing immunoprecipitated (IP) and input samples using statistical enrichment testing.
- R: `exomePeak2::exomePeak2()` for GC-bias aware peak calling
- CLI: `macs3 callpeak` as an alternative broad peak caller
## exomePeak2 (Recommended)
**Goal:** Identify m6A-enriched regions by comparing IP and input samples with GC-bias correction and replicate-aware statistical testing.
**Approach:** Provide IP and input BAM files along with a gene annotation to exomePeak2, which models read counts in sliding windows across the transcriptome and calls significant enrichment peaks.
```r
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
```bash
# 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
```r
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
```bash
# 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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