Prerequisites, data summary and availability

Software

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.

Data

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,

/scratch/gencore/datasets/chipseq_dataset.tar.gz

Getting the data and creating your analysis directory

  1. Open up a terminal
  2. Log in to your HPC (Dalma) 
    ssh yournetid@dalma.abudhabi.nyu.edu
  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
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