Nextflow enables scalable and reproducible scientific workflows using software containers such as Docker and Singularity, and Conda, a package and environment management system. It allows the adaptation of pipelines written in the most common scripting languages suach as such as R and Python. Its has a Domain Specific Language (DSL) that simplifies the implementation and the deployment of complex parallel and reactive workflows on clouds and clusters.
This lesson will also introduce nf-core: a framework that provides a community-driven, peer reviewed platform for the development of best practice analysis pipelines written in Nextflow.
This lesson motivates the use of Nextflow and nf-core as a development tool for building and sharing computational pipelines that facilitate reproducible (data) science workflows.
- The learner will understand the basic concepts of a Nextflow script, including channels, processes and operators.
- The learner will be able to write a Nextflow configuration file to alter the computational resources allocated to a process.
- The learner will write a multi-step workflow scripts to align, quantify, and perform QC on an RNA-Seq data in Nextflow DSL.
- The learner will use nf-core to run a community curated pipeline, on an RNA-Seq dataset
This is an intermediate lesson and assumes familiarity with the core materials covered in the Software Carpentry Lessons. In particular learners need to be familiar with material covered in The Unix Shell, Version Control with Git, and either Plotting and Programming in Python or R for Reproducible Scientific Analysis.