This repository contains **BioFlow-Insight**, a Python software tool. **BioFlow-Insight** automatically analyses Nextflow workflow code, extracting useful information, notably in the form of visual graphs illustrating the workflow's structure and its various steps.
This repository contains **BioFlow-Insight**, a Python software tool. **BioFlow-Insight** automatically analyses Nextflow workflow code, extracting useful information, notably in the form of visual graphs illustrating the workflow's structure and its various steps.
**BioFlow-Insight** is easily installable as a Python package (see here). It is also accessible as a web service (see[here](https://bioflow-insight.pasteur.cloud/)).
**BioFlow-Insight** is easily installable as a Python package (see here). It is also accessible as a free web service. For more information and to start using BioFlow-Insight, visit[here](https://bioflow-insight.pasteur.cloud/)(https://bioflow-insight.pasteur.cloud/).
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-[Input](#input)
-[Input](#input)
-[Output](#output)
-[Output](#output)
-[License](#license)
-[License](#license)
-[Funding](#funding)
## Installation
## Installation
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2. The second graph represents operations without any inputs, along with processes and their dependencies. This graph, called the *dependency graph without branch operations*, is obtained by removing the branch operations and linking the remaining elements if a path exists between them in the original specification graph.
2. The second graph represents operations without any inputs, along with processes and their dependencies. This graph, called the *dependency graph without branch operations*, is obtained by removing the branch operations and linking the remaining elements if a path exists between them in the original specification graph.
3. The final graph, called the *process dependency graph*, represents only processes and their dependencies. Similar to the latter, this graph is constructed by removing all operations, leaving only processes, and linking them based on their dependencies in the original specification graph.
3. The final graph, called the *process dependency graph*, represents only processes and their dependencies. Similar to the latter, this graph is constructed by removing all operations, leaving only processes, and linking them based on their dependencies in the original specification graph.
> For a more in-depth explanation of BioFlow-Insight functionnalities, visit its webpage [here](https://bioflow-insight.pasteur.cloud/) (https://bioflow-insight.pasteur.cloud/).
> To examplify **BioFlow-Insight** utilisation, let's use the rnaseq-nf workflow proposed by Nextflow (its source code can be found [here](https://github.com/nextflow-io/rnaseq-nf/tree/8253a586cc5a9679d37544ac54f72167cced324b)). Examples of the output are given below.
> To examplify **BioFlow-Insight** utilisation, let's use the rnaseq-nf workflow proposed by Nextflow (its source code can be found [here](https://github.com/nextflow-io/rnaseq-nf/tree/8253a586cc5a9679d37544ac54f72167cced324b)). Examples of the output are given below.
### Input
### Input
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This project is licensed under the [MIT License](https://opensource.org/licenses/MIT)
This project is licensed under the [MIT License](https://opensource.org/licenses/MIT)
TODO -> add license to git repo
## Funding
This work received support from the National Research Agency under the France 2030 program, with reference to ANR-22-PESN-0007.
