bio-epitranscriptomics-merip-preprocessing
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Then ask your AI: use the bio-epitranscriptomics-merip-preprocessing skill
What this skill tells your AI
The instructions your AI receives, as published by freedomintelligence/openclaw-medical-skills in skills/bio-epitranscriptomics-merip-preprocessing/SKILL.md and read by ahel’s review.
name: bio-epitranscriptomics-merip-preprocessing description: 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. tool_type: cli primary_tool: STAR measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
MeRIP-seq Preprocessing
Alignment with STAR
# 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
# 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
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
Signals
- GitHub stars
- 3k
- Forks
- 407
- Last commit
- Jul 2026
Advanced
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bio-epitranscriptomics-merip-preprocessing- Source
- github.com/freedomintelligence/openclaw-medical-skills