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 BioTender-max/awesome-bio-agent-skills --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.
## Version Compatibility Reference examples tested with: DESeq2 1.42+, MACS3 3.0+, STAR 2.7.11+, bedtools 2.31+, fastp 0.23+, samtools 1.19+ 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. # MeRIP-seq Pipeline **"Analyze my MeRIP-seq data from FASTQ to differential m6A peaks"** → Orchestrate read alignment (STAR), m6A peak calling (exomePeak2/MACS3), differential modification testing, and metagene/Guitar visualization of modification sites. ## 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
- Version Compatibility
- 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 BioTender-max/awesome-bio-agent-skills --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 BioTender-max/awesome-bio-agent-skills, a repository with 135 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.