Using prototyping to choose a bioinformatics workflow management system

Abstract

Workflow management systems represent, manage, and execute multistep computational analyses and offer many benefits to bioinformaticians. They provide a common language for describing analysis workflows, contributing to reproducibility and to building libraries of reusable components. They can support both incremental build and re-entrancy—the ability to selectively re-execute parts of a workflow in the presence of additional inputs or changes in configuration and to resume execution from where a workflow previously stopped. Many workflow management systems enhance portability by supporting the use of containers, high-performance computing (HPC) systems, and clouds. Most importantly, workflow management systems allow bioinformaticians to delegate how their workflows are run to the workflow management system and its developers. This frees the bioinformaticians to focus on what these workflows should do, on their data analyses, and on their science. RiboViz is a package to extract biological insight from ribosome profiling data to help advance understanding of protein synthesis. At the heart of RiboViz is an analysis workflow, implemented in a Python script. To conform to best practices for scientific computing which recommend the use of build tools to automate workflows and to reuse code instead of rewriting it, the authors reimplemented this workflow within a workflow management system. To select a workflow management system, a rapid survey of available systems was undertaken, and candidates were shortlisted: Snakemake, cwltool, Toil, and Nextflow. Each candidate was evaluated by quickly prototyping a subset of the RiboViz workflow, and Nextflow was chosen. The selection process took 10 person-days, a small cost for the assurance that Nextflow satisfied the authors’ requirements. The use of prototyping can offer a low-cost way of making a more informed selection of software to use within projects, rather than relying solely upon reviews and recommendations by others.

pyrpipe: a Python package for RNA-Seq workflows

“…We implemented within two workflow management systems, Snakemake ( 7 ) and NextFlow ( 8 ), selecting these specifically because they are widely used by the bioinformatics community ( 27 ). Snakemake and NextFlow were independently used to implement, manage and execute the pipeline for multiple RNA-Seq samples in parallel on a single cluster.…”

“…Toil ( 14 ) provides explicit application programming interfaces (APIs) for defining static or dynamic tasks and supports common workflow language (CWL) and multiple cloud environments ( 9 , 14 ). Hence, users need to make informed decisions if choosing a workflow management system ( 27 ) for .…”

“polishCLR: a Nextflow workflow for polishing PacBio CLR genome assemblies”

“…Increasing interest in workflow development systems that track data and software provenance, enable scalability and reproducibility, and re-entrant code (Wratten et al, 2021) have led to the development of several workflow languages, largely inspired by GNU Make (Amstutz et al, 2016; Köster & Rahmann, 2012; Stallman & McGrath, 1991). Nextflow is a Domain Specific Language (Di Tommaso et al, 2017) that currently leads workflow systems in terms of ease of scripting and submitting to cloud computing resources (Fjukstad & Bongo, 2017; Jackson et al, 2021; Leipzig, 2017; Spjuth et al, 2020). A key benefit of Nextflow compared to earlier workflow languages is being able to submit jobs to a local machine, an HPC, or cloud-based compute environments.…”

Design considerations for workflow management systems use in production genomics research and the clinic

“…A recent relevant paper is that of Jackson et al (2021). By rapid prototyping, they quickly evaluated Snakemake, CWL+CWL-tool, CWL+Toil, and Nextflow on a subset of RiboVis (Carja et al, 2017) workflows.…”