bio-clip-seq-clip-qc
Comprehensive quality control for CLIP-seq libraries (eCLIP, iCLIP, iCLIP2, PAR-CLIP) covering library complexity (preseq), FRiP, IDR replicate reproducibility, read-distribution metagene, SMInput vs IgG control rationale, rRNA / snoRNA contamination, fragment-length distribution, and ENCODE-compliance thresholds. Use when assessing whether a CLIP library passed, deciding lenient vs stringent peak thresholds, comparing replicates with IDR rescue and self-consistency ratios, or distinguishing failed IP from over-amplified library.
npx skills add BioTender-max/awesome-bio-agent-skills --skill clip-qc --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: preseq 3.2+, picard 3.1+, samtools 1.19+, bedtools 2.31+, deeptools 3.5+, idr 2.0.4+, MultiQC 1.21+, RSeQC 5.0+, pysam 0.22+, fastp 0.23+. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws unexpected errors, introspect the installed binary and adapt the example to match the actual CLI rather than retrying. # CLIP-seq Quality Control **"Did my CLIP library pass?"** -> Assess preprocessing retention, alignment rate, library complexity, replicate reproducibility (IDR), fraction reads in peaks (FRiP), read-distribution metagene, rRNA/snoRNA contamination, fragment-length distribution, and SMInput vs IP enrichment. ENCODE eCLIP compliance is the canonical bar: >= 1M unique fragments per replicate, IDR rescue and self-consistency ratios both < 2, FRiP >= 0.005 (narrow-binding), library complexity rising linearly with depth on preseq lc_extrap. A library can fail at any of these stages, and the failure mode determines whether the data is salvagea
- Version Compatibility
- QC Stage Hierarchy
- Library Complexity with preseq
- FRiP (Fraction Reads in Peaks)
- IDR for CLIP Reproducibility
- SMInput vs IgG Control: Which?
- Read Distribution Metagene
- Fragment-Length Distribution (Paired-End)
- Pre-Map rRNA / snoRNA Contamination Check
- Antibody Validation Sanity Check
- Per-Stage Failure Modes
- Gate 1: Preprocessing retention < 70%
- Gate 2: Alignment rate < 60%
- Gate 3: Library complexity < 1M unique
After alignment, BEFORE UMI dedup (preseq counts PCR duplicates) preseq lc_extrap \ sample_aligned.bam Output columns: TOTAL_READS EXPECTED_DISTINCT LOWER_0.95CI UPPER_0.95CI At 100M reads, EXPECTED_DISTINCT: picard direct estimate at current depth picard EstimateLibraryComplexity \ ESTIMATED_LIBRARY_SIZE > 5M = healthy CLIP library Reads in peaks (use stringent peaks: log2 FC >= 3, -log10 p >= 3)
What does the bio-clip-seq-clip-qc skill do?
Comprehensive quality control for CLIP-seq libraries (eCLIP, iCLIP, iCLIP2, PAR-CLIP) covering library complexity (preseq), FRiP, IDR replicate reproducibility, read-distribution metagene, SMInput vs IgG control rationale, rRNA / snoRNA contamination, fragment-length distribution, and ENCODE-compliance thresholds. Use when assessing whether a CLIP library passed, deciding lenient vs stringent peak thresholds, comparing replicates with IDR rescue and self-consistency ratios, or distinguishing failed IP from over-amplified library.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill clip-qc --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.