About

Overview

BioFlow-Insight is a open-source tool that generates graph structures from your Nextflow workflow's source code, illustrating each step of your workflow. It automatically analyses your Nextflow workflow code and reconstructs its structure without manual configuration or execution. Additionally, it can identify various types of errors within the code. For more information about BioFlow-Insight's capabilities, error handling, and guidelines, consult the Specification List.


Citing BioFlow-Insight

Please cite BioFlow-Insight in any research that uses or extends BioFlow-Insight.

To cite BioFlow-Insight, please use the following publication:

George Marchment, Bryan Brancotte, Marie Schmit, Frédéric Lemoine, Sarah Cohen-Boulakia, BioFlow-Insight: facilitating reuse of Nextflow workflows with structure reconstruction and visualization, NAR Genomics and Bioinformatics, Volume 6, Issue 3, September 2024, lqae092, https://doi.org/10.1093/nargab/lqae092


Implementation

BioFlow-Insight is implemented in Python 3 and employs an object-oriented programming approach. A list of its dependencies and its source code can be found on its GitLab source code page https://gitlab.liris.cnrs.fr/sharefair/bioflow-insight. BioFlow-Insight is also accessible as a Python package, easily installable via pip (see here).


Contributors

BioFlow-Insight was developed by George Marchment¹, in the context of a PhD at the LISN (University Paris Saclay). This work was supervised by Sarah Cohen-Boulakia¹ and Frédéric Lemoine²³. BioFlow-Insight’s web page was developed by Bryan Brancotte². Valuable insight was also provided by Marie Schmit³.

  1. Université Paris-Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, Orsay, France,
  2. Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris, France
  3. Institut Pasteur, Université Paris Cité, CNR Virus Des Infections Respiratoires, Paris, France


Contact

If you have any questions, comments or problems. Contact us at george.marchment(at)universite-paris-saclay.fr


Funding

This work received support from the National Research Agency under the France 2030 program, with reference to ANR-22-PESN-0007.