text: user285: 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 
  
  ABCB1
  ABCC1
  ABCC2
  SNAI2
  
  So how GATA1 occupancy is changed across all these target genes across 4 different treatment condition.
  
  So far what i have done
  
  Aligned the samples 
  Done peak calling
  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?
  
  user77: 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).
  
original_text: 285: <p>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). </p>
  
  <p>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 </p>
  
  <ol>
  <li>ABCB1</li>
  <li>ABCC1</li>
  <li>ABCC2</li>
  <li>SNAI2</li>
  </ol>
  
  <p>So how GATA1 occupancy is changed across all these target genes across 4 different treatment condition.</p>
  
  <p><a href="https://i.stack.imgur.com/TJ0I1.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/TJ0I1.jpg" alt="representative figure"></a></p>
  
  <p>So far what i have done</p>
  
  <ol>
  <li>Aligned the samples </li>
  <li>Done peak calling</li>
  <li>Annotated the peak </li>
  </ol>
  
  <p>I did steps 2 and 3 using homer.</p>
  
  <p>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?</p>
   77: <p>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.</p>
  
  <p>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).</p>
  