chip-seq-analysis

Overview

chip-seq-analysis covers chromatin immunoprecipitation sequencing from raw reads to biological interpretation: QC specific to ChIP (fragment size, enrichment metrics), peak calling (MACS2), differential binding (DiffBind/DESeq2), motif discovery, and annotation of peaks to regulatory function. ChIP-seq quality is dominated by the antibody — the analysis can only work with what the IP enriched.

When to use

• Experimental design: antibody validation, controls (input/IgG), replicate numbers, depth.
• QC: cross-correlation, FRiP, library complexity, strand cross-correlation.
• Peak calling: MACS2 parameters for narrow (TFs) vs broad (histone marks) peaks.
• Differential binding: DiffBind, proper count-based statistics.
• Motif discovery: HOMER, MEME — finding and validating motifs.
• Peak annotation: genomic distribution, target-gene assignment, overlap analysis.
• Visualization: genome browser tracks, heatmaps, metaplots.

Core concepts

• Antibody first. Validate by western blot + IP-western or, ideally, ChIP-qPCR on known positive/negative loci before sequencing. ENCODE antibody validation guidelines exist for a reason — most failed ChIP-seqs trace to unvalidated antibodies. Polyclonal lot variation is real; record lots.
• Controls. Input (sonicated chromatin, no IP) is the essential control — models local biases (copy number, chromatin accessibility, mappability). IgG controls for non-specific binding but doesn't replace input. Sequence input to comparable depth.
• Fragment size. Sonication/MNase to 200-500 bp; verify on Bioanalyzer before IP. Fragment size determines resolution — and MACS2's shifting model estimates it, but garbage in (kilobase fragments) means garbage out.
• QC metrics. NSC/RSC (normalized/relative strand cross-correlation — measures clustering of reads; ENCODE thresholds: NSC >1.05, RSC >0.8); FRiP (fraction of reads in peaks — the enrichment summary; good TF ChIP >1%, great >5%; histone marks higher); library complexity (PCR bottleneck coefficient — low complexity = over-amplified); replicate concordance. Check these before peak interpretation.
• Peak calling (MACS2). Narrow peaks (TFs): default MACS2 with q-value cutoff (q<0.01-0.05). Broad peaks (H3K27me3, H3K36me3): --broad mode. Always call against input control; don't call peaks on merged replicates then "validate" in individuals (call in each, assess overlap — IDR framework for replicate concordance).
• IDR (irreproducible discovery rate). Ranks peaks by consistency across replicates — the principled way to define a consensus peak set. ENCODE standard; use it instead of arbitrary overlap thresholds.
• Differential binding. DiffBind: consensus peak set → count reads per peak per sample → DESeq2/edgeR statistics. This is RNA-seq-style count modeling on peaks — same rules (replicates, FDR, effect sizes). Don't compare peak lists by Venn diagram overlap; test quantitatively.
• Motif discovery. HOMER or MEME-ChIP on top peaks (500-1000 strongest); expect the known motif for the factor (positive control — if the factor's own motif isn't enriched, question the experiment); de novo motifs need validation (don't name a "novel motif" from one experiment). Central enrichment of motifs within peaks supports direct binding.
• Annotation. Genomic distribution (promoter/intronic/intergenic vs expected); target gene assignment (nearest TSS is crude — use GREAT or activity-by-contact models for enhancers); overlap with other marks (chromatin states via ChromHMM). A peak list without functional annotation is a phone book.

Practical workflow

1. Validate antibody. IP-western/ChIP-qPCR before sequencing; record lot.
2. Prepare. Fragment to 200-500 bp; input control; ≥2 biological replicates; 20M+ reads for TFs, more for broad marks.
3. Align + QC. Bowtie2/BWA; deduplicate; compute NSC/RSC, FRiP (after preliminary peaks), complexity; check replicate correlation.
4. Call peaks. MACS2 (narrow or broad mode) vs input; IDR across replicates for consensus set.
5. Differential. DiffBind consensus → counts → DESeq2; FDR + fold-change thresholds.
6. Motifs. HOMER/MEME on top peaks; check known motif; central enrichment.
7. Annotate + visualize. Genomic distribution, target genes, browser tracks (normalized bigWigs), heatmaps/metaplots at peak sets.
8. Report. Antibody (vendor/catalog/lot), QC metrics, peak counts, IDR thresholds, GEO deposition.

Example command sketch:

macs2 callpeak -t chip.bam -c input.bam -f BAM -g hs -n tf_x -q 0.01
# broad marks:
macs2 callpeak -t h3k27me3.bam -c input.bam -f BAM -g hs -n k27 --broad -q 0.05

Common pitfalls

• Unvalidated antibody (enrichment of nothing, confidently peak-called).
• No input control (CNV/accessibility artifacts as "peaks").
• Merged-replicate peak calling without IDR.
• Venn diagrams instead of quantitative differential binding.
• Motif "discovery" without the known-motif positive control.
• Nearest-gene assignment for enhancer peaks (often wrong).
• Ignoring FRiP/NSC failures and interpreting anyway.