This course focuses on the development and execution of bioinformatics pipelines and on their optimization with regards to computing time and disk space. In an era where the data produced per-analysis is in the order of terabytes, simple serial bioinformatic pipelines are no longer feasible. Hence the need for scalable, high-performance parallelization and analysis tools which can easily cope with large-scale datasets. To this end, we will study the common performance bottlenecks emerging from everyday bioinformatic pipelines and see how to strike down the execution times for effective data analysis on current and future supercomputers.
As a case study, a transcriptome data analysis will be presented and re-implemented on the supercomputers of CINECA thanks to ad-hoc hands-on sessions aimed at applying the concepts explained in the course.


By the end of the course each student should be able to:

- Manage the transfer/download of huge data and/or large number of files from the local computer or public repositories to the Cineca platforms and vice versa
- Prepare the software environment to analyse big amount of biological data on a supercomputer;
- Run bioinformatic sotware on a supercomputer;
- Combine several bioinformatics applications into automated pipelines on a supercomputer;
- Have an overview of python data analysis framework.

Target audience:

Biologists, bioinformaticians and computer scientists interested in approaching large-scale NGS-data analysis for the first time.


Basic knowledge of python and shell command line. A very basic knowledge of biology is recommended but not required.