Prerequisites, data summary and availability


In terms of software, we are mainly using deeptools2, which is available to you by loading the “gencore” and “gencore_variant_detection” modules. We will also explore an alternative way of generating the CHiP-seq plots in R and Rstudio using the bioconductor package CoverageView.


As mentioned earlier, our exercise will focus on the data generated as part of the paper in (Xin et al). The data represents 2 conditions, knockout (KO) and wildtype (WT), with 2 different modifications, Brg1 and H3K9Me3. In addition, we have 1 Input for each of our conditions. So in total we have the 6 samples below,

  1. Sample_KO_Brg1_Chip

  2. Sample_KO_H3K9Me3_Chip

  3. Sample_KO_Input

  4. Sample_WT_Brg1_Chip

  5. Sample_WT_H3K9Me3_Chip

  6. Sample_WT_Input

The data can be found at,


Getting the data and creating your analysis directory

  1. Open up a terminal
  2. Log in to your HPC (Dalma)
  3. Change into your scratch directory
    cd $SCRATCH
  4. Copy the datasets
    cp /scratch/gencore/datasets/chipseq_dataset.tar.gz .
  5. Untar the dataset and cd to the analysis directory.
    tar -xvf chipseq_dataset.tar.gz
    cd chipseq_analysis
  6. Change into the bigWigs directory
    ls -lah bigWigs