bio-chipseq-qc
Assesses ChIP-seq quality across antibody specificity, fragmentation, enrichment, replicate concordance, and library complexity. Computes FRiP, NSC/RSC (phantompeakqualtools), library complexity (NRF/PBC1/PBC2), deepTools plotFingerprint (JS distance, AUC, synthetic JS), ChIPQC, IDR with ENCODE Nself/Nt rules, and detects hyper-ChIPable artifacts. Use when validating an antibody, diagnosing failed peak calls, deciding whether to proceed with downstream analysis, grading against ENCODE thresholds, or auditing replicate concordance.
npx skills add BioTender-max/awesome-bio-agent-skills --skill 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: deepTools 3.5+, phantompeakqualtools 1.2.2+, ChIPQC 1.42+, IDR 2.0.4+, samtools 1.19+, bedtools 2.31+, pysam 0.22+, pybedtools 0.9+, MACS2 2.2.9+, MACS3 3.0.4+. Verify versions before relying on numerical thresholds — phantompeakqualtools has known R-version compatibility issues with R ≥ 4.0 (use kundajelab fork or pin to R 3.6). # ChIP-seq Quality Control **"Should I trust this ChIP-seq experiment?"** -> Validate antibody, fragmentation, enrichment, replicate concordance, library complexity, and absence of hyper-ChIPable artifacts before committing to downstream peak calling and differential analysis. - CLI: `Rscript run_spp.R -c=chip.bam -out=cc.txt` (NSC/RSC), `plotFingerprint -b chip.bam input.bam` (enrichment shape), `idr --samples rep1.np rep2.np` (replicate IDR) - R: ChIPQC package (Carroll & Stark; computes the full ENCODE metric battery) - Python: pysam + pybedtools for custom FRiP and library-complexity metrics ChIP-seq fails for many independent reasons. The QC metrics below probe distinct failure modes — passing one metric does not rescue another. Antibody failure cannot be fixed by sequencing more. ## The Antibod
- Version Compatibility
- The Antibody Problem is the Real Problem
- Fragment-Size Distribution is a Free Diagnostic
- QC Metric Battery with ENCODE Thresholds
- Hyper-ChIPable Region Detection
- Computing the Battery
- FRiP
- NSC / RSC / fragment length (phantompeakqualtools)
- Library complexity
- deepTools plotFingerprint
- Replicate Spearman correlation
- ChIPQC R package
- IDR and Replicate Consistency Rules
- ENCODE 3 vs ENCODE 4 Thresholds (unchanged for most QC)
Quick diagnostic — count fragment sizes from properly-paired reads
samtools view -f 0x2 sample.bam | awk '{print $9}' | awk '$1>0' \
Top 1% input signal as cell-type-specific custom blacklist
multiBigwigSummary BED-file -b input.bw -o input_signal.npz \
awk 'NR > 1' input_per_gene.tsv | sort -k4,4nr | head -n $(($(wc -l < input_per_gene.tsv) / 100)) \
Intersect peaks against this list; flag peaks falling in hyper-ChIPable regions
bedtools intersect -a peaks.narrowPeak -b hyper_chipable.bed -u > suspicious_peaks.bed
Rscript run_spp.R -c=chip.bam -savp=qc/chip_cc.pdf -out=qc/chip_cc.txt
Output columns: filename | numReads | estFragLen | corr_estFragLen |
phantomPeak | corr_phantomPeak | argmin_corr | min_corr |What does the bio-chipseq-qc skill do?
Assesses ChIP-seq quality across antibody specificity, fragmentation, enrichment, replicate concordance, and library complexity. Computes FRiP, NSC/RSC (phantompeakqualtools), library complexity (NRF/PBC1/PBC2), deepTools plotFingerprint (JS distance, AUC, synthetic JS), ChIPQC, IDR with ENCODE Nself/Nt rules, and detects hyper-ChIPable artifacts. Use when validating an antibody, diagnosing failed peak calls, deciding whether to proceed with downstream analysis, grading against ENCODE thresholds, or auditing replicate concordance.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill 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 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.