Nextflow for reproducible and automated data analysis
This hands-on course introduces the principles and practice of building scalable and reproducible data analysis pipelines using Nextflow. As bioinformatics data volumes increase and analyses become more complex, workflow management tools play a key role in ensuring reproducibility and efficiency.
During the training, you will learn to develop and run pipelines on local machines, HPC clusters, and cloud platforms. Starting with the basics, the course guides you through creating a simple pipeline and enhancing it with Nextflow’s modern DSL2 syntax.
Learning outcomes
- Describe the core components of Nextflow (e.g., channels, processes, modules, workflows)
- Explain the roles of each component in pipeline development and the reproducibility aspect
- Construct a basic Nextflow pipeline using DSL2 syntax and execute it on a local machine.
- Modify configuration files to adapt pipeline parameters for different computing environments (e.g., local, cluster, cloud).
- Apply best practices for reproducibility and scalability in workflow design.
- Debug and refactor a simple pipeline to improve clarity, modularity, and performance.
Approach
The training combines theoretical sessions with hands-on activities. In the end, a small challenge invites you to apply all the skills you have learned.
Check this opportunity: Reproducible Data Analysis: an essential capability in modern science.
Event intended for
Researchers with no or little knowledge of containers or workflow pipelines.
Required skills
- Completion of an HPC course is mandatory for individuals without prior experience in High Performance Computing before enrolling.
- You're familiar with doing bioinformatics on the command line and with Docker containers.
- If you have no experience with the command line, we strongly recommend following the Linux initiation training first or the self-paced e-learning.
- If you have no experience with Docker, you can follow our training "Containerize data analysis with Docker & Apptainer".