Bio epitranscriptomics merip preprocessing
Skill FridrichMethod/awesome-skills/skills/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.From its SKILL.md
npx -y skills add FridrichMethod/awesome-skills --skill bio-epitranscriptomics-merip-preprocessingAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
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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
What ships with it: 3 files
5.1 KB alongside SKILL.md, 2 of them executable
examples/
- align_merip.shruns885 B
- merip_qc.pyruns2.8 KB
- usage-guide.md1.4 KB