Generate workflow environment

Load workflow environment with sample data into your current working directory. The sample data are described here.


Alternatively, this can be done from the command-line as follows:

Rscript -e "systemPipeRdata::genWorkenvir(workflow='chipseq')"

In the workflow environments generated by genWorkenvir all data inputs are stored in a data/ directory and all analysis results will be written to a separate results/ directory, while the systemPipeChIPseq.Rmd script and the targets file are expected to be located in the parent directory. The R session is expected to run from this parent directory. Additional parameter files are stored under param/.

To work with real data, users want to organize their own data similarly and substitute all test data for their own data. To rerun an established workflow on new data, the initial targets file along with the corresponding FASTQ files are usually the only inputs the user needs to provide.

Run workflow

Now open the R markdown script systemPipeChIPseq.Rmdin your R IDE (e.g. vim-r or RStudio) and run the workflow as outlined below.

Run R session on computer node

After opening the Rmd file of this workflow in Vim and attaching a connected R session via the F2 (or other) key, use the following command sequence to run your R session on a computer node.

q("no") # closes R session on head node
srun --x11 --partition=short --mem=2gb --cpus-per-task 4 --ntasks 1 --time 2:00:00 --pty bash -l
module load R/3.3.0

Now check whether your R session is running on a computer node of the cluster and assess your environment.

system("hostname") # should return name of a compute node starting with i or c 
getwd() # checks current working directory of R session
dir() # returns content of current working directory

The systemPipeR package needs to be loaded to perform the analysis steps shown in this report (H Backman et al., 2016).


If applicable users can load custom functions not provided by systemPipeR. Skip this step if this is not the case.


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