| name | bio-workflows-chipseq-pipeline |
| description | Orchestrates the end-to-end ChIP-seq pipeline from FASTQ to blacklist-filtered, annotated peaks, chaining fastp QC, Bowtie2 alignment, pre-dedup library-complexity QC (NRF/PBC), duplicate removal, chrM + ENCODE-blacklist filtering, MACS3 peak calling against a matched input, IDR/consensus reproducibility, deepTools signal tracks, and ChIPseeker annotation. Use when committing the reference build + blacklist version + effective genome size once, pairing each IP with its matched control, computing complexity metrics BEFORE dedup, choosing narrow vs broad and MACS3 vs SEACR/Genrich, keeping per-replicate peaks for IDR, or avoiding depth-normalization that erases a spike-in global shift. Hands mechanism to the chip-seq component skills; not a re-teach of any single step. |
| tool_type | mixed |
| primary_tool | MACS3 |
| workflow | true |
| depends_on | ["read-qc/fastp-workflow","read-alignment/bowtie2-alignment","alignment-files/duplicate-handling","chip-seq/chipseq-qc","chip-seq/peak-calling","chip-seq/peak-annotation","chip-seq/differential-binding","chip-seq/chipseq-visualization","chip-seq/motif-analysis"] |
| qc_checkpoints | [{"after_qc":"Q30 >85%, adapter content <5%"},{"after_alignment":"Mapping rate >80%, unique mapping >70%"},{"before_dedup":"NRF >0.8, PBC1 >0.8 (computed on the PRE-dedup BAM; after dedup the metric is meaningless)"},{"after_peaks":"FRiP >1% (TF) or >5% (sharp histone; broad marks run lower); NSC >1.05; RSC >0.8; fingerprint separates IP from input"},{"after_idr":"IDR rescue ratio max(Np,Nt)/min and self-consistency ratio max(N1,N2)/min both <=2 (ENCODE); IDR run on PER-REPLICATE peaks"}] |
Version Compatibility
Reference examples tested with: Bowtie2 2.5.3+, MACS3 3.0+, HOMER 4.11+, bedtools 2.31+, deepTools 3.5+, fastp 0.23+, samtools 1.19+, ChIPseeker 1.38+
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.
Note: macs3 callpeak -f BAMPE uses real fragment lengths and IGNORES --shift/--extsize/--nomodel (those apply to single-end -f BAM); the -g shortcut (hs/mm) sets the effective genome size and must match the build/read length. Confirm in-tool before quoting.
ChIP-seq Pipeline
"Process my ChIP-seq data from FASTQ to annotated peaks" -> Chain QC/trim, alignment, pre-dedup complexity QC, dedup + blacklist filtering, control-matched peak calling, reproducibility, signal tracks, and annotation.
- CLI + R: fastp -> bowtie2 -> (NRF/PBC pre-dedup) -> samtools markdup -> chrM/blacklist filter -> macs3 callpeak (IP vs input) -> IDR -> bamCoverage -> ChIPseeker
This is a workflow skill: it owns the chaining decisions and hand-offs, not the internals of any one step. Every step below cross-references the component skill that teaches its mechanism.
The governing principle
A ChIP-seq peakset is decided at four seams, not inside the caller.
- The reference build + blacklist version + effective genome size is one coordinate commitment made once and inherited by everything downstream — peak coordinates, signal-track scaling, and every overlap. Blacklist filtering is a committed pipeline step, not optional cleanup: ENCODE-blacklisted regions (satellite/rDNA/high-signal artifacts) produce reproducible false peaks in every dataset regardless of biology, so they are removed before calling (Amemiya 2019).
- An IP is only interpretable against its matched control — the control IS the enrichment background. Calling peaks without the right input/IgG fabricates peaks at open/accessible and copy-number-amplified regions. Pair each IP with its control at the calling step.
- Library-complexity QC (NRF/PBC1/PBC2) is computed on the PRE-dedup BAM. After
markdup -r the duplicates are gone, so computing complexity afterward reads ~1.0 and is meaningless. Compute it on the filtered, position-sorted BAM before removing duplicates.
