bio-workflows-merip-pipeline
End-to-end MeRIP-seq analysis from FASTQ to m6A peaks and differential methylation. Use when analyzing epitranscriptomic m6A modifications from immunoprecipitation data.
npx skills add majiayu000/claude-skill-registry --skill merip-pipeline --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# MeRIP-seq Pipeline ## Pipeline Overview ``` FASTQ → QC → Align IP+Input → Peak calling → Annotation → Differential → Visualization ``` ## Step 1: Quality Control ```bash fastp -i IP_R1.fq.gz -I IP_R2.fq.gz \ -o IP_R1_trimmed.fq.gz -O IP_R2_trimmed.fq.gz \ --json IP_fastp.json --html IP_fastp.html fastp -i Input_R1.fq.gz -I Input_R2.fq.gz \ -o Input_R1_trimmed.fq.gz -O Input_R2_trimmed.fq.gz \ --json Input_fastp.json --html Input_fastp.html ``` ## Step 2: Alignment ```bash STAR --genomeDir star_index \ --readFilesIn IP_R1_trimmed.fq.gz IP_R2_trimmed.fq.gz \ --readFilesCommand zcat \ --outSAMtype BAM SortedByCoordinate \ --outFileNamePrefix IP_ STAR --genomeDir star_index \ --readFilesIn Input_R1_trimmed.fq.gz Input_R2_trimmed.fq.gz \ --readFilesCommand zcat \ --outSAMtype BAM SortedByCoordinate \ --outFileNamePrefix Input_ samtools index IP_Aligned.sortedByCoord.out.bam samtools index Input_Aligned.sortedByCoord.out.bam ``` ## Step 3: Peak Calling with exomePeak2 ```r library(exomePeak2) library(TxDb.Hsapiens.UCSC.hg38.knownGene) result <- exomePeak2( bam_ip = c('IP_rep1.bam', 'IP_rep2.bam'), bam_input = c('Input_rep1.bam', 'Input_rep2.bam'), txdb = TxDb.Hsapiens.UCSC.hg38.knownGe
- Pipeline Overview
- Step 1: Quality Control
- Step 2: Alignment
- Step 3: Peak Calling with exomePeak2
- Step 4: Alternative Peak Calling with MACS3
- Step 5: Motif Analysis
- Step 6: Differential Methylation
- Step 7: Peak Annotation
- Step 8: Metagene Visualization
- Complete Bash Pipeline
- QC Checkpoints
- Output Files
- Related Skills
fastp -i IP_R1.fq.gz -I IP_R2.fq.gz \ fastp -i Input_R1.fq.gz -I Input_R2.fq.gz \ STAR --genomeDir star_index \ samtools index IP_Aligned.sortedByCoord.out.bam samtools index Input_Aligned.sortedByCoord.out.bam macs3 callpeak -t IP.bam -c Input.bam \ macs3 bdgdiff --t1 IP_treat_pileup.bdg --c1 IP_control_lambda.bdg \ findMotifsGenome.pl m6a_peaks.bed hg38 motif_output/ -size 100 -S 5 bedtools getfasta -fi genome.fa -bed m6a_peaks.bed -fo peak_sequences.fa homer2 known -i peak_sequences.fa -m DRACH.motif -o motif_scan.txt
What does the bio-workflows-merip-pipeline skill do?
End-to-end MeRIP-seq analysis from FASTQ to m6A peaks and differential methylation. Use when analyzing epitranscriptomic m6A modifications from immunoprecipitation data.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill merip-pipeline --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.