bio-workflows-clip-pipeline
End-to-end CLIP-seq pipeline from FASTQ to ENCODE-compliant binding sites, single-nucleotide crosslink maps, annotation, motifs, and (optionally) differential binding. Use when running the full Yeo lab eCLIP / iCLIP / iCLIP2 / iCLIP3 / irCLIP / PAR-CLIP analysis with SMInput control, protocol-specific UMI extraction, ENCODE STAR parameters, CLIPper or Skipper peak calling with stringent log2 FC and -log10 p thresholds, IDR rescue and self-consistency QC, and downstream motif registration with mCross or PEKA.
npx skills add BioTender-max/awesome-bio-agent-skills --skill clip-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: umi_tools 1.1.5+, cutadapt 4.6+, fastp 0.23+, STAR 2.7.11b+, samtools 1.19+, bedtools 2.31+, CLIPper 2.0+, Skipper (commit 2023.05+), PureCLIP 1.3.1+, HOMER 4.11+, ChIPseeker 1.40+, preseq 3.2+, picard 3.1+, idr 2.0.4+, MultiQC 1.21+. Before using code patterns, verify installed versions match. If versions differ: - CLI: `<tool> --version` then `<tool> --help` to confirm flags - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters If code throws unexpected errors, introspect the installed tool and adapt the example rather than retrying. # CLIP-seq End-to-End Pipeline **"Analyze my CLIP-seq data from raw FASTQ to ENCODE-compliant binding sites"** -> Orchestrate protocol-specific UMI extraction, 3'-only adapter trimming (preserving the R2 5' truncation = crosslink site -1), ENCODE STAR alignment, UMI-based deduplication, library complexity QC, peak calling against SMInput with stringent thresholds (log2 FC >= 3 AND -log10 p >= 3), single-nucleotide crosslink-site detection, ChIPseeker annotation with CLIP-appropriate `tssRegion`,
- Version Compatibility
- Pipeline Overview
- CLIP Variant Selection
- Step 1: Quality Control of Raw FASTQ
- Step 2: Preprocessing (Protocol-Specific)
- Step 3: Alignment (ENCODE STAR Block)
- Step 4: QC (Five Gates)
- Step 5: Peak Calling
- Step 6: Single-Nucleotide Crosslink-Site Detection
- Step 7: IDR Across Replicates
- Step 8: Binding-Site Annotation
- Step 9: Motif Analysis (De Novo + CL-Registered)
- Step 10: Differential Binding (Optional, Across Conditions)
- Quality Checkpoints
Initial QC fastqc raw_R1.fq.gz raw_R2.fq.gz -o qc/raw/ Inspect first 12 bases of 100 reads to verify UMI pattern matches the prep zcat raw_R1.fq.gz | awk 'NR%4==2' | head -100 | cut -c1-12 | sort | uniq -c | sort -rn | head Random barcode positions show ~25% per base; library barcodes are fixed See clip-seq/clip-preprocessing for protocol-specific patterns umi_tools extract \ Pass 1: 3' adapter on both reads cutadapt \ R1.umi.fq.gz R2.umi.fq.gz \
What does the bio-workflows-clip-pipeline skill do?
End-to-end CLIP-seq pipeline from FASTQ to ENCODE-compliant binding sites, single-nucleotide crosslink maps, annotation, motifs, and (optionally) differential binding. Use when running the full Yeo lab eCLIP / iCLIP / iCLIP2 / iCLIP3 / irCLIP / PAR-CLIP analysis with SMInput control, protocol-specific UMI extraction, ENCODE STAR parameters, CLIPper or Skipper peak calling with stringent log2 FC and -log10 p thresholds, IDR rescue and self-consistency QC, and downstream motif registration with mCross or PEKA.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill clip-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.