Assay
ChIP-seq: sanity checks and pitfalls
Antibody pull-down enriches DNA bound by a factor or carrying a histone mark; reads are aligned and peaks are called against an input or IgG control. Sharp peaks (transcription factors) and broad domains (H3K27me3, H3K36me3) need different caller settings, and the absence of a matched input turns copy-number variation and open-chromatin bias into fake peaks. Duplicate rate, fraction of reads in peaks and the cross-correlation profile tell you whether the immunoprecipitation worked before any biology is interpreted.
Who this is for: Transcription-factor and chromatin biologists asking where a factor binds or how a histone mark changes between conditions. Their datasets are old enough that genome builds and chromosome naming conventions get mixed, and the input control is the piece most often missing or mishandled.
- How to Choose a Normalization Method in ChIP-seq
RPKM, CPM, and median-of-ratios all assume nothing changed genome-wide, an assumption that quietly kills real histone-mark shifts before you ever see them.
- How to Read a P-Value Histogram in ChIP-seq
Flat with a spike, a hill, a U, or a pile-up near 1: each shape of your differential binding p-values points at a specific broken assumption, and you can see it in one line of R.
- How to Call Peaks You Can Trust in ChIP-seq
A matched input control and the ENCODE blacklist are the two things standing between your peak list and a stack of copy-number and repeat artifacts you'd otherwise write up as binding sites.