bio-chipseq-qc
ChIP-seq quality control metrics including FRiP (Fraction of Reads in Peaks), cross-correlation analysis (NSC/RSC), library complexity, and IDR (Irreproducibility Discovery Rate) for replicate concordance. Use to assess experiment quality before downstream analysis. Use when assessing ChIP-seq data quality metrics.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-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: MACS3 3.0+, Subread 2.0+, bedtools 2.31+, deepTools 3.5+, pybedtools 0.9+, pysam 0.22+, samtools 1.19+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - 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. # ChIP-seq Quality Control **"Assess the quality of my ChIP-seq experiment"** → Compute FRiP, cross-correlation (NSC/RSC), library complexity, and IDR replicate concordance to evaluate enrichment success. - CLI: `deeptools plotFingerprint`, `phantompeakqualtools run_spp.R` - Python: `pysam` + `pybedtools` for custom QC metrics Quality metrics for assessing ChIP-seq experiment success and replicate reproducibility. ## FRiP (Fraction of Reads in Peaks) **Goal:** Quantify enrichment strength by measuring the proportion of reads falling within called peaks. **Approach:** Count re
- Version Compatibility
- FRiP (Fraction of Reads in Peaks)
- Calculate FRiP with bedtools
- Calculate FRiP with featureCounts
- Calculate FRiP with pysam
- FRiP Thresholds
- Cross-Correlation Analysis (NSC/RSC)
- Run phantompeakqualtools
- Interpret NSC and RSC
- NSC/RSC Thresholds
- Plot Cross-Correlation in R
- Library Complexity (NRF, PBC1, PBC2)
- Calculate with bedtools
- Library Complexity Thresholds
Count reads in peaks
Calculate FRiP
echo "FRiP: $frip"
Convert peaks to SAF format
awk 'BEGIN{OFS="\t"} {print $4, $1, $2, $3, "."}' peaks.narrowPeak > peaks.saf
featureCounts -a peaks.saf -F SAF -o peak_counts.txt chip.bam
FRiP from summary
grep -v "^#" peak_counts.txt.summary
Run SPP cross-correlation analysis
Rscript run_spp.R \What does the bio-chipseq-qc skill do?
ChIP-seq quality control metrics including FRiP (Fraction of Reads in Peaks), cross-correlation analysis (NSC/RSC), library complexity, and IDR (Irreproducibility Discovery Rate) for replicate concordance. Use to assess experiment quality before downstream analysis. Use when assessing ChIP-seq data quality metrics.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-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 FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 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.