The occupancy of SMARCD3 in the target genes listed below. I want to see average, normalized ChIP-seq signal at the promoter proximal region (1000bp upstream and downstream of the TSS).

I have 4 different experimental conditions (overlayed in one plot, per target gene using different color codes) where we want to visualize the changes in occupancy of GATA1 under all the conditions at individual gene promoters. The following are our target genes

  1. ABCB1
  2. ABCC1
  3. ABCC2
  4. SNAI2

So how GATA1 occupancy is changed across all these target genes across 4 different treatment condition.

representative figure

So far what i have done

  1. Aligned the samples
  2. Done peak calling
  3. Annotated the peak

I did steps 2 and 3 using homer.

Now after all this how do I find the occupancy of a TF with its target genes? Do I get the information from my peaks annotated output or from the peak files?


1 Answer 1


I would ignore peak calling for this and instead compute enrichment of ChIP/input for the genome (e.g., with deepTools or presumably homer) and then plot it for the genes of interest individually (e.g., using IGV or pyGenomeTracks) or as a group (e.g., with computeMatrix). If the peaks are obvious and you trust your peak calling then sure you can just use that instead, but given that that's prone to error it's usually better to simply use normalized signal.

Regarding actually looking at occupancy, I don't think that's useful. Occupancy isn't binary. If you're instead wanting to associate peaks with genes then using the nearest gene is common (it's not great, but it's not terrible either).

  • $\begingroup$ actually i looked into your deeptool as many publications used to similar analysis but not sure how it was done. " Occupancy isn't binary. " yes i now get it occupancy can;t be binary , " If you're instead wanting to associate peaks with genes then using the nearest gene is common" how do i perform this part in terms of tool can i do it in deeptools? $\endgroup$
    – kcm
    Jun 19, 2019 at 19:40
  • 1
    $\begingroup$ For finding the nearest gene, just use bedtools nearest from bedtools. $\endgroup$
    – Devon Ryan
    Jun 19, 2019 at 20:42
  • $\begingroup$ sciencedirect.com/science/article/pii/S109727651930228X in this paper this figure (G) Metaplots of canonical PRC1 (PCGF2, CBX7, and PHC1) cChIP-seq at classical Polycomb chromatin domains (n = 2,096) in Pcgf4−/−;Pcgf2fl/fl ESCs (UNT and OHT), in this paper did they get into this? did they use bedtools nearest as well to find the gene as they write "recruits canonical PRC1 to Polycomb target genes in order to compact chromatin " $\endgroup$
    – kcm
    Jun 20, 2019 at 6:18
  • $\begingroup$ They didn't do any gene assignments in that subpanel, they just input known polycomb domains. $\endgroup$
    – Devon Ryan
    Jun 20, 2019 at 6:39
  • 1
    $\begingroup$ No, they didn’t need or use genes for subpanel G. $\endgroup$
    – Devon Ryan
    Jun 20, 2019 at 9:46

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