Scientific Computing resources and High Performance Computing 

Getting Started

Getting an account 

- Description of different types of account and how to get them


Connecting to the cluster 

- Step-by-step 

- Using X11 forwarding: This does not provide you with a remote desktop, but allow you to launch graphical applications (e.g, rstudio) on the cluster and forward their windows to your local workstation. 

Running jobs

- submit jobs, check status etc

- slurm QOS and partition (set job resources and priorities etc)

File System

- Introduction to the default system

- Adding $WORK and $SCRATCH to your .bashrc 

# Set up some useful environment 
export WORK='/work/upzenk'
export SCRATCH='/scratch/<username>' 

- Addtional location: our group own server. To mount our group server to the cluster see instruction. In our case:

gio mount smb://intranet\;<username>


Using the cluster 

Setup some softwares

- define $PATH where we installed cellranger, cellranger-atac, cellranger-arc, rstudio etc 

cd $WORK
source source_softwares_setup


Runing Jupyter notebook 

Step 1: Activate the venvs on /work/upzenk: Python virtual environment with common packages installed (e.g, scanpy). 

cd $WORK
source softwares/python_venvs/venv-single-cell/bin/activate

Step 2: Running Jupyter notebook. This jupyter notebook session can use all the packages you have in the virtual environment. 

Install new packages 



Using Rstudio 

# login with -X11 forwarding
ssh -X <username>

# prepare rstudio
module load gcc r
cd $WORK
source source_softwares_setup

# login to a node; specify the mem, core and time you want
Sinteract -m 16G

# open Rstudio

# check library path 

Install R packages 

module load r

# check R library path
R .libPaths()

# we install packages in '/work/upzenk/softwares/r/r_packages'
R .libPaths( c( .libPaths(), "/path/to/the/dir") )

# install the packages
R install.packages('dummy', lib = '/path/to/the/dir')

Tips for bioinformatics tools


- Set up the environment: first you need to load modules: gcc, snakemake; then load the tools useful for your pipeline (e.g, bwa, samtools). 

- After setting up the environment, you can run the very minimum example: (do not need to follow the installation part).


- A tool trailored for epigenetics data analysis, based on snakemake and python (some R scripts are used as well). See documentation.

- createIndices