bio-chipseq-peak-calling

Version Compatibility

Reference examples tested with: MACS3 3.0.4+, MACS2 2.2.9+, HOMER 4.11+, SPP 1.16+, samtools 1.19+, bedtools 2.31+, IDR 2.0.4+.

Before running, verify versions: <tool> --version and <tool> --help to confirm flags. If a flag is missing, check the changelog — MACS2->MACS3 is API-compatible for callpeak but predictd, bdgpeakcall, and hmmratac differ.

ChIP-seq Peak Calling

"Identify protein-DNA binding sites from ChIP-seq alignments" -> Detect statistically enriched genomic regions by comparing IP signal to input control (or genomic background), with peak shape (narrow/broad) determined by target biology (TF vs histone mark).

• CLI (ENCODE TF default): macs2 callpeak -t chip.bam -c input.bam -f BAM -g hs -n sample --keep-dup all -p 1e-2
• CLI (ENCODE histone default): same with --broad --broad-cutoff 0.1 for H3K27me3, H3K9me3, H3K36me3
• CLI (alternative): macs3 callpeak ... (API-identical, active development), HOMER findPeaks tags/ -style histone -i input_tags/, SPP via phantompeakqualtools wrapper

ENCODE TF pipeline still uses SPP for peak ranking + IDR, with MACS2 producing the signal tracks. Histone pipeline uses MACS2 + naive overlap (IDR is too conservative for histone signal dynamic range). MACS3 is the actively maintained successor; MACS2 receives only bug fixes.

Critical Pre-Call Validation

Before any peak calling, three things must be true or the output is unreliable:

1. Antibody validated — KO/KD orthogonal control, peptide-array specificity for histone modifications, or vendor-provided CRISPR-validated lot (Epicypher, CST). "ChIP-grade" marketing is not validation. See chipseq-qc.
2. Fragment-size distribution is sane — TF ChIP should show sub-nucleosomal (~50-100 bp) enrichment; histone ChIP should show clean mono- (~150) and di-nucleosomal (~300) peaks. Flat distribution = over-sonication; rescue is impossible. Check via samtools view -f 0x2 sample.bam | awk '{print $9}' | sort | uniq -c.
3. Input control matches — Sonicated input is biased toward open chromatin; MNase input toward nucleosomes. Input from a different library prep batch or fragmentation method introduces bias that subtraction cannot fix.

Algorithmic Taxonomy

Tool	Model	Treats fragments as	Strength	Fails when
MACS3/MACS2 callpeak	Dynamic local Poisson (max of genome-wide, 1kb, 5kb, 10kb lambda) + BH-FDR	Single-end shifts; PE fragments via BAMPE	Mature, fast, ENCODE-default, narrow + broad modes, integrated signal tracks	Confounds NFR with broad accessible domains; default narrow mode segments broad enrichment; assumes most genome NOT enriched (breaks for genome-wide marks)
SPP (Kharchenko 2008)	Strand cross-correlation peak detection + Poisson fold-enrichment	Single-end with cross-corr-derived shift	ENCODE TF caller; integrated NSC/RSC QC; robust for sharp TF peaks	Underperforms for broad marks; older R codebase; phantompeakqualtools wrapper has R-version compatibility issues
HOMER -style factor	Fixed-width peaks + three sequential filters (control / local / clonal)	Tag positions; auto-estimated width	Fast on tag directories; clonal filter -C removes PCR-artifact peaks	Less calibrated p-values; fixed width clips variable-width factor binding
HOMER -style histone	Variable-width region stitching (500 bp blocks, 1000 bp gap merging); L=0 (no local enrichment)	Tag positions	Captures variable-width histone enrichment; Omnipeak benchmark (Shpynov & Artyomov 2026): outperforms -style factor for histone marks including H3K4me3	Less sensitive than MACS for very sharp TF binding
Genrich -y (ChIP mode)	q-value on log-transformed p-value, joint replicate model	Whole fragments (PE intervals)	Joint replicate analysis; chrM exclusion via -e chrM; auto blacklist via -E	Less peer-reviewed than MACS/SPP; thin literature; control handling less mature
MACS3 hmmratac	3-state HMM on fragment-size signal	Fragment-size classes	Best for ATAC, not ChIP	Wrong tool for ChIP; ChIP fragment-size distribution doesn't drive useful HMM states
SEACR (Meers 2019)	Empirical threshold on signal block totals	Bedgraph signal blocks	Designed for sparse CUT&RUN/CUT&Tag data; "stringent" mode with IgG strongly preferred	Not for traditional ChIP-seq (assumes near-zero background); see cut-and-run-tag
LanceOtron (Hentges 2022)	CNN trained on ENCODE peaks	bigWig signal	Competitive for both narrow and broad without parameter tuning	Newer; less validated; web-only or pip install

For CUT&RUN / CUT&Tag specifically, see chip-seq/cut-and-run-tag — protocol differences (lower depth, IgG-only control, E. coli spike-in carryover) drive different caller choice (MACS2 + SEACR consensus, not MACS3 alone).

Decision: Narrow vs Broad

Driven by target biology, not preference. Calling broad mode does not make a sharp signal broad; it changes how MACS stitches adjacent enrichment.

Target	Mode	Why
Transcription factors (CTCF, p53, GATA1, FOXA1)	Narrow (default)	Discrete motif binding produces sharp peaks
H3K4me3, H3K27ac at promoters/enhancers	Narrow	Localized at regulatory elements
H3K4me1 at enhancers	Narrow or broad-cutoff 0.1	Variable; check published data for the cell type
H3K36me3, H3K79me2 (elongation)	Broad	Deposited across active gene bodies (5-50 kb domains)
H3K27me3, H3K9me3 (repressive)	Broad	Spread across 10-100+ kb domains
H4K20me3 (constitutive het)	Broad	Heterochromatin domains
Pol II (RNAPII)	Narrow at promoter + broad option for elongation profile	Two separate analyses if doing elongation biology

For HOMER: use -style histone for ALL histone marks (Omnipeak benchmark, Shpynov & Artyomov 2026 NAR 54:gkaf1454); -style factor ONLY for transcription factors.

Decision: Model vs --nomodel

MACS2/3 fragment-size modeling needs ≥100 paired plus/minus enrichment regions within --mfold (default [5, 50]). Silent failure produces wrong fragment size and warped peaks — always inspect _model.r output.

Condition	Model?	Fallback
Whole-genome, ≥1M treatment reads, narrow TF	Yes	--mfold 3 50 if fails
Paired-end with -f BAMPE	N/A	Fragment size from mate pairs
Single chromosome or targeted capture	No	--nomodel --extsize <data-derived or mark default>
Low read count (<500k)	No	Same
Broad histone mark	Either	Mark-type default if no estimate available

When --nomodel is required, choose --extsize in priority order: (1) cross-correlation estimate from phantompeakqualtools (ENCODE standard, gives NSC/RSC simultaneously); (2) macs3 predictd -i chip.bam -g hs and read stderr; (3) mark-type fallback (147 for nucleosome-proximal marks, 200 for broader marks).

Effective Genome Size — Often Wrong, Always Matters

-g hs (2.7e9) and -g mm (1.87e9) are decade-old approximations. Modern read-length-matched values (deepTools effectiveGenomeSize table):

Genome	Read length	Effective size
hg38	50 bp	2.701e9
hg38	75 bp	2.748e9
hg38	100 bp	2.806e9
hg38	150 bp	2.862e9
mm10	50 bp	2.308e9
mm10	100 bp	2.467e9

Wrong size shifts every q-value but rarely peak ranks. For subset data (single chromosome, targeted), provide numeric -g <bp>; the shorthand inflates lambda_BG by 60× and produces false positives at low-signal regions.

Hyper-ChIPable Regions Are a Persistent Artifact

Teytelman 2013 (PNAS) and Park 2013 (PLoS One) demonstrated that highly-transcribed genes (rRNA, tRNA, histone gene cluster, snoRNA hosts, mitochondrial-encoded genes, abundant housekeeping loci) appear "bound" in ChIP-seq with untagged GFP, no antibody, or non-existent targets. ENCODE blacklist v2 catches repeat-driven artifacts but NOT these hyper-ChIPable transcribed regions.

Always interpret peaks at rRNA loci, tRNA clusters, replication-dependent histone genes (HIST1/2 clusters), mitochondrial DNA, and the top-1% input-signal regions with skepticism. For rigorous claims: (1) require motif enrichment at the peak (artifact has no motif); (2) require KO/KD signal loss; (3) build a cell-type-specific blacklist from the top 1% of input signal and intersect-out.

Pipeline Reference: ENCODE TF vs Histone

TF pipeline (uses SPP for peak ranking):

# Per-replicate (loose) — IDR tightens downstream
macs2 callpeak -t rep1.tagAlign.gz -c input.tagAlign.gz \
    -f BED -g hs -n rep1 \
    --nomodel --shift 0 --extsize {fraglen_from_xcor} \
    --keep-dup all -B --SPMR -p 1e-2

# Repeat for rep2, pooled, and pseudoreplicates (split each rep into halves)
# Score peaks by signalValue, sort, run IDR (see Replicate Handling below)

Histone pipeline (uses MACS2 broad / narrow + naive overlap):

# Broad marks: H3K27me3, H3K9me3, H3K36me3
macs2 callpeak -t rep1.tagAlign.gz -c input.tagAlign.gz \
    -f BED -g hs -n rep1 \
    --broad --broad-cutoff 0.1 \
    --nomodel --shift 0 --extsize {fraglen} \
    --keep-dup all -B --SPMR -p 1e-2

# Naive overlap: a peak passes if it appears in ≥2 of N replicates
# with ≥40% reciprocal overlap (ENCODE default, often misquoted as 50%)
bedtools intersect -a rep1.broadPeak -b rep2.broadPeak -f 0.40 -r -u > naive_overlap.bed

--keep-dup all is intentional in the ENCODE pattern: duplicates were already filtered upstream by MarkDuplicates + samtools view -F 1804 -q 30. -p 1e-2 is permissive because IDR (TF) or overlap (histone) tightens downstream.

Replicate Handling: IDR vs Naive Overlap

ENCODE rules (Landt 2012 Genome Res):

TFs use IDR. Run on signal-ranked peaks (sort by -k8,8nr p-value; -k7,7nr signal works for SPP but breaks for MACS pile-up if libraries differ).

sort -k8,8nr rep1.narrowPeak > rep1.sorted
sort -k8,8nr rep2.narrowPeak > rep2.sorted

idr --samples rep1.sorted rep2.sorted \
    --input-file-type narrowPeak --rank p.value \
    --idr-threshold 0.05 --output-file true_reps.idr --plot

ENCODE Nself/Nt consistency rule (often misremembered):

• Nt = IDR-passing peaks across true biological replicates (threshold 0.05)
• Nself (per rep) = IDR-passing peaks across pseudoreplicates of one library (threshold 0.10)
• Library passes if max(N1self, N2self) / min(N1self, N2self) ≤ 2 AND max(Nt, max(Nself)) / min(Nt, min(Nself)) ≤ 2
• Both ratios > 2: library rejected

Histones use naive overlap. IDR's high-vs-low-rank assumption breaks for histone dynamic range. Naive overlap: pool peaks, require each to appear in ≥2 replicates with ≥40% reciprocal overlap.

ENCODE 3 vs ENCODE 4 Differences

Feature	ENCODE 3	ENCODE 4
TF peak ranker	SPP	SPP (unchanged)
Histone caller	MACS2	MACS2 (MACS3 not yet adopted)
Aligner	bwa-mem	bwa-mem (chromap evaluated; not yet swapped)
Blacklist	v1 (ENCODE DAC, unpublished resource)	v2 (Amemiya 2019)
TF significance	-p 1e-2 + IDR @ 0.05	Same
Histone significance	-p 1e-2 + naive overlap	Same
Effective genome size	hs/mm shorthand	deepTools read-length-tabulated
Pseudoreplicate IDR threshold	0.10 self-consistency	0.10 self-consistency

ENCODE 4 outputs are NOT numerically comparable to ENCODE 3 on the same BAM (blacklist change + genome size update shift peak counts ~3-10%).

Per-Tool Failure Modes

MACS2/3 -- Silent fragment-size model failure

Trigger: Sparse signal, low replicate depth, or saturated samples; _model.r plot never inspected.

Mechanism: Model needs ≥100 paired plus/minus enriched regions in --mfold range. Below threshold, MACS picks an arbitrary fragment size (often 50 or 1000 bp), producing miscentered or oversized peaks. Stderr shows a warning that gets ignored.

Symptom: Peak summits shifted relative to known motif positions by hundreds of bp; visual inspection in IGV shows peaks displaced from pile-up centers.

Fix: Inspect <sample>_model.r — if peaks look reasonable, accept; if degenerate, widen with --mfold 3 50 or switch to --nomodel --extsize <data-derived>. For consistency across samples in a study, always use --nomodel --extsize {fraglen} with cross-correlation-derived fraglen (ENCODE pattern).

MACS2/3 -- Confounded narrow vs broad on intermediate marks

Trigger: Marks of intermediate breadth (H3K4me1, H3K9ac) called with default narrow mode.

Mechanism: Default narrow mode fragments wide enrichment into multiple sub-peaks; --broad over-stitches.

Symptom: Peak count 3-5× higher than published for same cell type; mean peak width < 200 bp at known enhancer regions.

Fix: For H3K4me1, try --broad --broad-cutoff 0.1 and compare; for H3K9ac, narrow mode typically OK. Always cross-reference published peak counts for the cell type and antibody lot.

MACS2/3 -- --call-summits double-counts

Trigger: Narrow mode + --call-summits flag.

Mechanism: MACS adds sub-peak summits at multi-mode pile-ups; broad-shouldered peaks get split into 2-3 entries.

Symptom: Peak count inflated; same genomic region appears as 2-3 adjacent peaks in narrowPeak output.

Fix: Drop --call-summits unless deliberately analyzing multi-mode binding (rare); merge bedtools merge -d 200 if needed post-hoc.

HOMER -- Wrong style for histones

Trigger: -style factor used for histone marks.

Mechanism: Factor mode uses fixed-width peaks with local enrichment filter -L 4 that eliminates broad signal.

Symptom: Far fewer peaks than expected for H3K4me3/H3K27ac/H3K27me3; missed enrichment at known regions.

Fix: Use -style histone for ALL histone marks (Omnipeak benchmark, Shpynov & Artyomov 2026); reserve -style factor for TFs only.

SPP / phantompeakqualtools -- R version incompatibility

Trigger: Running phantompeakqualtools wrapper script with R ≥ 4.0.

Mechanism: spp R package has unmaintained dependencies; some functions silently fail or return NaN for NSC/RSC.

Fix: Use conda env pinned to R 3.6 + spp 1.16; or use kundajelab/phantompeakqualtools fork (current); or substitute deepTools plotFingerprint for QC and MACS-derived fragment length.

chromap aligner -- Pre-applied shift double-counts

Trigger: Using chromap (fast aligner) output as MACS input with --shift -75 --extsize 150.

Mechanism: chromap pre-applies a Tn5/cut-site shift before fragment output (designed for ATAC); ChIP cut-site reasoning doesn't apply but the shift still happens silently.

Symptom: Peaks shifted ~5-10 bp from bwa-mem output at the same locus.

Fix: When using chromap, drop downstream shift OR use chromap's --no-correction. For ChIP, bwa-mem or bowtie2 are safer defaults until ENCODE switches.

Reconciliation: When Callers Disagree

Pattern	Likely cause	Action
MACS finds peak; HOMER misses	HOMER local-enrichment filter (-L 4) removed it at low-signal regions; or -style factor clipped a histone peak	Re-run HOMER with -style histone -L 0 for histones; if persists, trust MACS
HOMER finds peak; MACS misses	Clonal filter -C 2 retained PCR artifact peaks; or HOMER's auto-width captured something MACS narrow mode segmented	Check if MACS broad mode rescues; check IGV for visual confirmation
SPP and MACS narrow peaks differ by 10-50 bp summit	Different fragment-size estimates (SPP uses cross-corr; MACS models from data)	Use same fragment size for both: ENCODE pattern --nomodel --extsize {xcor_fraglen}
MACS narrow + MACS broad on same data: 10× peak count difference	Expected — broad mode stitches subpeaks within 1 kb gap	Use narrow for differential analysis (consistent units); broad for domain annotation
Per-rep MACS calls peak; pooled MACS does not	One replicate dominates; pooling smooths local lambda	Trust pooled + IDR over per-replicate counts
Replicate count differs >2×	One replicate failed	Check FRiP, NSC, library complexity per replicate; do NOT average — drop the failing replicate or repeat

Operational rule for publication-grade: TFs require IDR ≤ 0.05 on true reps AND Nt/Nself ratios ≤ 2. Histones require naive overlap ≥2 reps with ≥40% reciprocal overlap. Both require FRiP, NSC, RSC, and library complexity thresholds met. See chipseq-qc.

Common Errors

Error / symptom	Cause	Solution
0 peaks called	Wrong genome size on subset data; wrong -f for input format; swapped treatment/control	Provide numeric -g; match -f to file type (BAM/BAMPE/BED); verify -t is enriched sample
Peak count >> 500k	Did not deduplicate; chrM not removed; -q too loose; hyper-ChIPable artifacts dominate	Filter samtools view -F 1804 -q 30; remove chrM; tighten to -q 0.01; blacklist top-1% input regions
Peaks shifted from motif by ~75 bp	--shift not set for -f BAM; or fragment-size model wrong	Add --shift 0 --extsize {fraglen}; or check _model.r
--shift/--extsize ignored warning	Used -f BAMPE with these flags	Switch to -f BAM for ENCODE pattern, or accept that BAMPE uses true fragment spans
IDR returns 0 reproducible peaks	Sorted by wrong column; ranks effectively random	sort -k8,8nr (p-value descending) on each peakset
Naive overlap returns few peaks	Set -f 0.5 -r (50% reciprocal) — too strict	Use -f 0.40 -r (ENCODE default)
FRiP < 1%	Bad ChIP (antibody, fragmentation, depth); peaks called on noise	Re-validate antibody with KO/KD; check fragment-size distribution; do not proceed

References

• Park PJ 2009 Nat Rev Genet 10:669 (foundational review)
• Landt SG et al 2012 Genome Res 22:1813 (ENCODE/modENCODE guidelines, IDR Nself rule)
• Zhang Y et al 2008 Genome Biol 9:R137 (MACS)
• Kharchenko PV et al 2008 Nat Biotechnol 26:1351 (SPP)
• Heinz S et al 2010 Mol Cell 38:576 (HOMER)
• Li Q et al 2011 Ann Appl Stat 5:1752 (IDR framework)
• Teytelman L et al 2013 PNAS 110:18602 (hyper-ChIPable regions)
• Park D et al 2013 PLoS One 8:e83506 (independent hyper-ChIPable confirmation)
• Amemiya HM et al 2019 Sci Rep 9:9354 (ENCODE blacklist v2)
• ENCODE ChIP-seq pipeline v2.1.6 (github.com/ENCODE-DCC/chip-seq-pipeline)
• Shpynov O & Artyomov MN 2026 Nucleic Acids Res 54:gkaf1454 (Omnipeak; benchmarks HOMER -style histone vs factor for histone marks)

Related Skills

• chip-seq/chipseq-qc - Fragment-size diagnostic, FRiP, NSC/RSC, antibody validation
• chip-seq/cut-and-run-tag - SEACR + MACS for CUT&RUN/CUT&Tag (different QC, lower depth)
• chip-seq/spike-in-normalization - When global signal shifts expected (HDACi, BETi, EZH2i)
• chip-seq/differential-binding - DiffBind/csaw downstream of peak calling
• chip-seq/peak-annotation - Annotate peaks to genes and cCREs
• chip-seq/motif-analysis - Discover and scan binding motifs in peaks
• chip-seq/super-enhancers - Stitch H3K27ac peaks into super-enhancer calls
• atac-seq/atac-peak-calling - ATAC-specific shift/extend; no input control
• alignment-files/sam-bam-basics - Pre-call BAM filtering and deduplication
• genome-intervals/interval-arithmetic - Peak intersection and overlap