BioFlow-Insight: facilitating reuse of Nextflow workflows with structure reconstruction and visualization.

Bioinformatics workflows are increasingly used for sharing analyses, serving as a cornerstone for enhancing the reproducibility and shareability of bioinformatics analyses. In particular, Nextflow is a commonly used workflow system, permitting the creation of large workflows while offering substantial flexibility. An increasing number of Nextflow workflows are being shared on repositories such as GitHub. However, this tremendous opportunity to reuse existing code remains largely underutilized. In cause, the increasing complexity of workflows constitute a major obstacle to code reuse. Consequently, there is a rising need for tools that can help bioinformaticians extract valuable information from their own and others' workflows. To facilitate workflow inspection and reuse, we developed BioFlow-Insight to automatically analyze the code of Nextflow workflows and generate useful information, particularly in the form of visual graphs depicting the workflow's structure and representing its individual analysis steps. BioFlow-Insight is an open-source tool, available as both a command-line interface and a web service. It is accessible at https://pypi.org/project/bioflow-insight/ and https://bioflow-insight.pasteur.cloud/.

Developing and reusing bioinformatics data analysis pipelines using scientific workflow systems.

Data analysis pipelines are now established as an effective means for specifying and executing bioinformatics data analysis and experiments. While scripting languages, particularly Python, R and notebooks, are popular and sufficient for developing small-scale pipelines that are often intended for a single user, it is now widely recognized that they are by no means enough to support the development of large-scale, shareable, maintainable and reusable pipelines capable of handling large volumes of data and running on high performance computing clusters. This review outlines the key requirements for building large-scale data pipelines and provides a mapping of existing solutions that fulfill them. We then highlight the benefits of using scientific workflow systems to get modular, reproducible and reusable bioinformatics data analysis pipelines. We finally discuss current workflow reuse practices based on an empirical study we performed on a large collection of workflows.