Speaker
Description
Deriving scientifically sound conclusions from microscopy experiments typically requires batch analysis of large image data sets. It is critical that these analysis workflows are reproducible. In addition, it is advantageous of the workflows are scalable and can be readily deployed on high performance compute infrastructures.
Here, we will present how the established Nextflow workflow management system can be used in the context of bioimage analysis. We will detail several advantages, such as combining analysis tools developed in different programming languages, using conda and containers for the reproducible deployment of the analysis steps, using the Nextflow reporting tools for workflow optimisation, leveraging inbuilt error handling strategies, and convenient deployment either locally and on a slurm managed computer cluster. Finally we will discuss how the nf-core specifications could allow our community to develop a modular analysis ecosystem with shareable tools and workflows.
| Authors | Christian Tischer* |
|---|---|
| Keywords | Nextflow, Batch analysis, Reproducible, High performance computing |
