In an era of generation of large datasets and complex scientific analyses, ensuring the reproducibility of data analyses has become paramount. Workflow management systems have emerged as a key solution to this challenge, offering structured, automated, and scalable frameworks for handling data processing tasks, allowing for the development of scientific workflows. However, while they are becoming more popular, workflow management systems have not yet gained wide adoption within the scientific community, largely due to established practices and the perceived high learning curve associated with their use.
This tutorial aims at demonstrating the critical role of workflow management systems in implementing reproducible data analyses, with an emphasis on their capacity to encapsulate heterogeneous code, manage software environments, scale with the data size, and leverage heterogeneous computational resources efficiently. To do so, we will use the Nextflow workflow system and a viral genome sequence reconstruction pipeline as a use case. This will demonstrate the fundamentals of Nextflow and illustrate how it can be used to easily implement, execute, and share a simple workflow.