Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

We support various ways of running bioinformatic applications on our CCB cluster.


We provide R Studio and Jupyter Notebook which provide interactive web interfaces so that you can visually observe the computation outcome.

TCR Distance Analysis
Example 1: computing beta distance of samples, courtesy of Benjamin McMaster.


Once you are comfortable with the interactive sessions, you can turn your interactive tasks into batch tasks, and run them in batch through SLURM. On JADE, there are different queues from with task length from 1 day to 7 days. As a smooth transition, you can first acquire interactive SLURM sessions from the interactive queue, and then submit the real tasks to batch queues once you are comfortable with the result. Further complimentary support is available upon request.

Catch-up Example 2
Example 2: running CATCH-UP on JADE, courtesy of Simone Riva.


As on a typical HPC system, estimating the resources is one of the trickiest parts. For this, we automatically run profiling tools when a job starts to run on JADE. You can retrospectively review the resource usage so you can adjust the resource requirements of the upcoming jobs.


We mainly support the use of the following languages and runtime environments:

  • Python: as one of the most popular programming languages, we prepared commonly used bioinformatic software modules which can be used on Jupyter Notebook or in a batch job.
  • R: as one of the most popular analytical languages, we prepared a set of commonly used bioinformatic software modules which can be used on Rstudio or in a batch job.
  • C / C++: as one of the most performant programming languages, we have industry-standard compilers prepared for compiling the analytical programs that our researchers need. We accumulate these software packages and make them available to our users

We also have environments such as Perl and Java that users can rely on.