It establishes standards for programmatically creating a series of dependent computational steps and facilitates their execution on various local and cloud resources.

Custom scripts may suffice when developing new methods or infrequently running particular analyses, but scale poorly to complex task successions or many samples.

They are accomplished by special purpose programs, so-called workflow executors, which ensure predictable and reproducible behavior in various computing environments.

[9][10] Typically, scientific workflow systems initially present a steep learning challenge as all their features and complexities are built on in addition to the actual analysis.

However, the standards and abstraction imposed by workflow systems ultimately improve the traceability of analysis steps, which is particularly relevant when collaborating on pipeline development, as is customary in scientific settings.

[13] Processes and entire workflows are programmed in a domain-specific language (DSL) which is provided by Nextflow which is based on Apache Groovy.

Workflows and single processes can utilize containers for their execution across different computing environments, eliminating the need for complex installation and configuration routines.

[32] Led by Phil Ewels, at the Swedish National Genomics Infrastructure at the time,[33][34] nf-core focuses on ensuring reproducibility and portability of pipelines across different hardware, operating systems, and software versions.

These domains include Drug screening,[46] Diffusion magnetic resonance imaging (dMRI) in radiology,[47] and mass spectrometry data processing,[48][49][50] the latter with a particular focus on proteomics[51] [52] [53] [54] [55] [56] [57] [58] Galaxy Snakemake