# Bio Epitranscriptomics Merip Preprocessing

> Align and QC MeRIP-seq IP and input samples for m6A analysis. Use when preparing MeRIP-seq data for peak calling or differential methylation analysis.

- Skill: `gabrielmoreira/bio-epitranscriptomics-merip-preprocessing` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add gabrielmoreira/bio-epitranscriptomics-merip-preprocessing`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/bio-epitranscriptomics-merip-preprocessing/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/gabrielmoreira/bio-epitranscriptomics-merip-preprocessing

---


## Version Compatibility

Reference examples tested with: STAR 2.7.11+, deepTools 3.5+, samtools 1.19+

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- 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.

# MeRIP-seq Preprocessing

**"Preprocess my MeRIP-seq IP and input samples"** → Align and QC methylated RNA immunoprecipitation sequencing data, comparing IP enrichment to input for downstream m6A peak calling.
- CLI: `STAR` for splice-aware alignment, `samtools` for post-processing, `deepTools` for QC

## Alignment with STAR

**Goal:** Align MeRIP-seq IP and input samples to the genome with splice-aware mapping for downstream peak calling.

**Approach:** Build a STAR genome index with gene annotations, then loop through all IP and input samples to produce coordinate-sorted BAM files.

```bash
# Build index (once)
STAR --runMode genomeGenerate \
    --genomeDir star_index \
    --genomeFastaFiles genome.fa \
    --sjdbGTFfile genes.gtf

# Align IP and input samples
for sample in IP_rep1 IP_rep2 Input_rep1 Input_rep2; do
    STAR --genomeDir star_index \
        --readFilesIn ${sample}_R1.fastq.gz ${sample}_R2.fastq.gz \
        --readFilesCommand zcat \
        --outSAMtype BAM SortedByCoordinate \
        --outFileNamePrefix ${sample}_
done
```

## QC Metrics

```bash
# Index BAMs
for bam in *Aligned.sortedByCoord.out.bam; do
    samtools index $bam
done

# Check IP enrichment
# Good MeRIP: IP should have peaks, input should be uniform
samtools flagstat IP_rep1_Aligned.sortedByCoord.out.bam
```

## IP/Input Correlation

```python
import deeptools.plotCorrelation as pc

# Check replicate correlation
multiBamSummary bins \
    -b IP_rep1.bam IP_rep2.bam Input_rep1.bam Input_rep2.bam \
    -o results.npz

plotCorrelation -in results.npz \
    --corMethod spearman \
    -o correlation.png
```

## Related Skills

- read-qc/quality-reports - Raw read quality assessment
- read-alignment/star-alignment - General alignment concepts
- m6a-peak-calling - Next step after preprocessing