VueGen is a Python package that automates the creation of scientific reports.

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Table of contents:

• About the project
• Installation
• Execution
• GUI
• Case studies
• Web application deployment
• License
• Contributing
• Credits and acknowledgements
• Citation
• Contact and feedback

About the project

VueGen is a tool that automates the creation of reports from bioinformatics outputs, allowing researchers with minimal coding experience to communicate their results effectively. With VueGen, users can produce reports by simply specifying a directory containing output files, such as plots, tables, networks, Markdown text, HTML components, and API calls, along with the report format. Supported formats include documents (PDF, HTML, DOCX, ODT), presentations (PPTX, Reveal.js), Jupyter notebooks, and Streamlit web applications.

A YAML configuration file is generated from the directory to define the structure of the report. Users can customize the report by modifying the configuration file, or they can create their own configuration file instead of passing a directory as input. The configuration file specifies the structure of the report, including sections, subsections, and various components such as plots, dataframes, markdown, html, and API calls.

An overview of the VueGen workflow is shown in the figure below:

We created a schema diagram to illustrates the structure of the configuration file and the relationships between its elements:

Also, the class diagram for the project's current version is presented below to show the architecture and relationships between classes:

An extended version of the class diagram with attributes and methods is available here.

The VueGen documentation is available at vuegen.readthedocs.io, where you can find detailed information of the package’s classes and functions, installation and execution instructions, and case studies to demonstrate its functionality.

Installation

[!TIP] It is recommended to install VueGen inside a virtual environment to manage depenendencies and avoid conflicts with existing packages. You can use the virtual environment manager of your choice, such as poetry, conda, or pipenv.

Pip

VueGen is available on PyPI and can be installed using pip:

bash pip install vuegen

You can also install the package for development by cloning this repository and running the following command:

[!WARNING] We assume you are in the root directory of the cloned repository when running this command. Otherwise, you need to specify the path to the vuegen directory.

bash pip install -e .

Conda

VueGen is also available on Bioconda and can be installed using conda:

bash conda install -c bioconda -c conda-forge vuegen

Dependencies

VueGen uses Quarto to generate various report types. The pip insallation includes quarto using the quarto-cli Python library. To test if quarto is installed in your computer, run the following command:

bash quarto check

[!TIP] If quarto is not installed, you can download the command-line interface from the Quarto website for your operating system.

For PDF reports, you need to have a LaTeX distribution installed. This can be done with quarto using the following command:

bash quarto install tinytex

[!TIP] Also, you can add the --quarto_checks argument to the VueGen command to check and install the required dependencies automatically.

Docker

If you prefer not to install VueGen on your system, a pre-configured Docker container is available. It includes all dependencies, ensuring a fully reproducible execution environment. See the Execution section for details on running VueGen with Docker. The official Docker images are available at quay.io/dtubiosustaindsp/vuegen. The Dockerfiles to build the images are available here.

Nextflow and nf-core

VueGen is also available as a nf-core module, customised for compatibility with the Nextflow environment. This module is designed to automate report generation from outputs produced by other modules, subworkflows, or pipelines. Asumming that you have nextflow and nf-core installed, you can use the following command to install the nf-core module:

bash nf-core modules install vuegen

[!NOTE] You can read the offical documentation for the nf-core module here. Also, the source code and additional details are available in the nf-VueGen repository.

Execution

[!IMPORTANT] Here we use the Earth_microbiome_vuegen_demo_notebook directory and the Earth_microbiome_vuegen_demo_notebook.yaml configuration file as examples, which are available in the docs/example_data and docs/example_config_files folders, respectively. Make sure to clone the VueGen's GitHub reposiotry to access these contents, or use your own directory and configuration file.

Run VueGen using a directory with the following command:

bash vuegen --directory docs/example_data/Earth_microbiome_vuegen_demo_notebook --report_type streamlit

[!NOTE] By default, the streamlit_autorun argument is set to False, but you can use it in case you want to automatically run the streamlit app. You can also specify the output directory with the --output_directory argumument, which defaults to the current working directory. See all available arguments with the --help option.

Folder structure

Your input directory should follow a nested folder structure, where first-level folders are treated as sections and second-level folders as subsections, containing the components (plots, tables, networks, Markdown text, and HTML files). If the component files are in the first-level folders, an overview subsection will be created automatically.

Here is an example layout:

report_folder/ ├── section1/ │ ├── table1.tsv │ └── subsection1/ │ ├── table2.csv │ ├── image1.png │ └── chart.json ├── section2/ │ ├── image2.jpg │ ├── subsection1/ │ │ ├── summary_table.xls │ │ └── network_plot.graphml │ └── subsection2/ │ ├── report.html │ └── summary.md

The titles for sections, subsections, and components are extracted from the corresponding folder and file names, and afterward, users can add descriptions, captions, and other details to the configuration file. Component types are inferred from the file extensions and names. The order of sections, subsections, and components can be defined using numerical suffixes in folder and file names.

Configuration file

It's also possible to provide a configuration file instead of a directory:

bash vuegen --config docs/example_config_files/Earth_microbiome_vuegen_demo_notebook.yaml --report_type streamlit

If a configuration file is given, users can specify titles and descriptions for sections and subsections, as well as component paths and required attributes, such as file format and delimiter for dataframes, plot types, and other details.

The component paths in the configuration file can be absolute or relative to the execution directory. In the examples, we assume that the working directory is the docs folder, so the paths are relative to it. If you run VueGen from another directory, you need to adjust the paths accordingly.

The current report types supported by VueGen are:

• Streamlit
• HTML
• PDF
• DOCX
• ODT
• Reveal.js
• PPTX
• Jupyter

Running VueGen with Docker

Instead of installing VueGen locally, you can run it directly from a Docker container with the following command:

bash docker run --rm \ -v "$(pwd)/docs/example_data/Earth_microbiome_vuegen_demo_notebook:/home/appuser/Earth_microbiome_vuegen_demo_notebook" \ -v "$(pwd)/output_docker:/home/appuser/streamlit_report" \ quay.io/dtu_biosustain_dsp/vuegen:v0.3.2-docker --directory /home/appuser/Earth_microbiome_vuegen_demo_notebook --report_type streamlit

Running VueGen with Nextflow and nf-core

To run VueGen as a nf-core module, you should create a Nextflow pipeline and include the VueGen module in your workflow. Here is a main.nf example:

```groovy

!/usr/bin/env nextflow

include { VUEGEN } from './modules/nf-core/vuegen/'

workflow { // Create a channel for the report type reporttypech = Channel.value(params.report_type)

// Handle configuration file and directory inputs
if (params.config) {
    file_ch = Channel.fromPath(params.config)
    input_type_ch = Channel.value('config')
    output_ch = VUEGEN(input_type_ch, file_ch, report_type_ch)

} else if (params.directory) {
    dir_ch = Channel.fromPath(params.directory, type: 'dir', followLinks: true)
    input_type_ch = Channel.value('directory')
    output_ch = VUEGEN(input_type_ch, dir_ch, report_type_ch)

}

} ```

You can run the pipeline with the following command:

bash nextflow run main.nf --directory docs/example_data/Basic_example_vuegen_demo_notebook --report_type html

[!NOTE] You can read the offical documentation for the nf-core module here. Also, the source code and additional details are available in the nf-VueGen repository.

GUI

We have a simple GUI for VueGen that can be run locally or through a standalone executable.

Local GUI

To use the local GUI, you should clone this repository and install the required dependencies. You can do this by running the following command in the root directory of the cloned repository:

bash pip install '.[gui]'

Then, you should move to the gui folder and execute the app.py Python file:

bash python app.py

Bundled GUI

The bundle GUI with the VueGen package is available under the latest releases. You will need to unzip the file and run vuegen_gui in the unpacked main folder. Most dependencies are included into the bundle using PyInstaller.

Streamlit works out of the box as a purely Python based package. For the rest of report types you will have to have a Python 3.12 installation with the jupyter package installed, as quarto needs to start a kernel for execution. This is also true if you install quarto globally on your machine.

[!TIP] It is advisable to create a virtual environment to manage depenendencies and avoid conflicts with existing packages. You can use the virtual environment manager of your choice, such as poetry, conda, or pipenv. We recommend using miniforge to install Python and the conda package manager.

We assume you have installed the miniforge distribution for your machine (MacOS with arm64/ apple silicon or x8664/ intel or Windows x8664). Also, download the latest vuegen_gui bundle from the releases page according to your operating system.

You can create a new conda environment with Python 3.12 and the jupyter and vuegen package:

bash conda create -n vuegen_gui -c conda-forge -c bioconda python=3.12 jupyter vuegen

The exact Python version used for the release is specified on the releases page as we observed that sometimes the exact Python version is required to run the GUI.

[!WARNING] If you have errors with the vuegen package, you can install it separately using pip, as explained in the installation section.

conda create -n vuegen_gui -c conda-forge -c bioconda python=3.12 jupyter conda activate vuegen_gui pip install vuegen

Then, activate the environment:

bash conda activate vuegen_gui

Now, you can list all conda environments to find the location of the vuegen_gui environment:

bash conda info -e

On MacOS, you need to add a bin to the path:

bash /Users/user/miniforge3/envs/vuegen_gui/bin

On Windows, you can use the path as displayed by conda info -e:

bash C:\Users\user\miniforge3\envs\vuegen_gui

[!NOTE] On Windows a base installation of miniforge with jupyter might work because the app can see your entire Path, which is not the case on MacOS.

More information regarding the app and builds can be found in the GUI README.

Case studies

VueGen’s functionality is demonstrated through various case studies:

1. Predefined Directory

This introductory case study uses a predefined directory with plots, dataframes, Markdown, and HTML components. Users can generate reports in different formats and modify the configuration file to customize the report structure.

🔗

[!NOTE] The configuration file is available in the docs/example_config_files folder, and the directory with example data is in the docs/example_data folder.

2. Earth Microbiome Project Data

This advanced case study demonstrates the application of VueGen in a real-world scenario using data from the Earth Microbiome Project (EMP). The EMP is an initiative to characterize global microbial taxonomic and functional diversity. The notebook process the EMP data, create plots, dataframes, and other components, and organize outputs within a directory to produce reports. Report content and structure can be adapted by modifying the configuration file. Each report consists of sections on exploratory data analysis, metagenomics, and network analysis.

🔗

[!NOTE] The EMP case study is available online as HTML and Streamlit reports. The configuration file is available in the docs/example_config_files folder, and the directory with example data is in the docs/example_data folder.

3. APICall Component

This case study focuses on the APICall component, which enables interaction with external APIs by using HTTP methods such as GET and POST. The retrieved data is displayed in the report, allowing users to integrate external data sources into their anlyses. This component is restricted to Streamlit reports.

🔗

[!NOTE] A configuration file example for the apicall component is available in the docs/example_config_files folder.

4. ChatBot Component

This case study highlights VueGen’s capability to embed a chatbot component into a report subsection, enabling interactive conversations inside the report. This component is streamlit-specific and is not available for other report types.

🔗

Two API modes are supported:

• Ollama-style streaming chat completion: If a model parameter is specified in the config file, VueGen assumes the chatbot is using Ollama’s /api/chat endpoint. Messages are handled as chat history, and the assistant responses are streamed in real time for a smooth and responsive experience. This mode supports LLMs such as llama3, deepsek, or mistral.

[!TIP] See Ollama’s website for more details.

• Standard prompt-response API: If no model is provided, VueGen uses a simpler prompt-response flow. A single prompt is sent to an endpoint, and a structured JSON object is expected in return. Currently, the response can include:
  • text: the main textual reply
  • links: a list of source URLs (optional)
  • HTML content: an HTML snippet with a Pyvis network visualization (optional)

This response structure is currently customized for an internal knowledge graph assistant, but VueGen is being actively developed to support more flexible and general-purpose response formats in future releases.

[!NOTE] A configuration file example for the chatbot component is available in the docs/example_config_files folder.

Web application deployment

Once a Streamlit report is generated, it can be deployed as a web application to make it accessible online. There are multiple ways to achieve this:

Streamlit Community Cloud

Deploy your report easily using Streamlit Cloud, as demonstrated in the EMP VueGen Demo. The process involves moving the necessary scripts, data, and a requirements.txt file into a GitHub repository. Then, the app can be deployed via the Streamlit Cloud interface. The deployment example is available in the streamlit-report-example branch.

Standalone Executables

Convert your Streamlit application into a desktop app by packaging it as an executable file for different operating systems. A detailed explanation of this process can be found in this Streamlit forum post.

Stlite

Run Streamlit apps directly in the browser with stlite, a WebAssembly port of Streamlit powered by Pyodide, eliminating the need for a server. It also allows packaging apps as standalone desktop executables using stlite desktop.

These options provide flexibility depending on whether the goal is online accessibility, lightweight execution, or local application distribution.

License

The code in this repository is licensed under the MIT License, allowing you to use, modify, and distribute it freely as long as you include the original copyright and license notice.

The documentation and other creative content are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License, meaning you are free to share and adapt it with proper attribution.

Full details for both licenses can be found in the LICENSE file.

Contributing

VueGen is an open-source project, and we welcome contributions of all kinds via GitHub issues and pull requests. You can report bugs, suggest improvements, propose new features, or implement changes. Please follow the guidelines in the CONTRIBUTING file to ensure that your contribution is easily integrated into the project.

Credits and acknowledgements

• VueGen was developed by the Multiomics Network Analytics Group (MoNA) at the Novo Nordisk Foundation Center for Biosustainability (DTU Biosustain).
• VueGen relies on the work of numerous open-source projects like Streamlit, Quarto, and others. A big thank you to their authors for making this possible!
• The vuegen logo was designed based on an image created by Scriberia for The Turing Way Community, which is shared under a CC-BY licence. The original image can be found at Zenodo.

Citation

If you use VueGen in your research or publications, please cite it as follows:

APA:

Ayala-Ruano, S., Webel, H., & Santos, A. (2025). VueGen: Automating the generation of scientific reports. Bioinformatics Advances, vbaf149. https://doi.org/10.1093/bioadv/vbaf149

BibTeX:

bibtex @article{10.1093/bioadv/vbaf149, author = {Ayala-Ruano, Sebastian and Webel, Henry and Santos, Alberto}, title = {VueGen: Automating the generation of scientific reports}, journal = {Bioinformatics Advances}, pages = {vbaf149}, year = {2025}, month = {06}, issn = {2635-0041}, doi = {10.1093/bioadv/vbaf149}, url = {https://doi.org/10.1093/bioadv/vbaf149}, eprint = {https://academic.oup.com/bioinformaticsadvances/advance-article-pdf/doi/ 10.1093/bioadv/vbaf149/63568410/vbaf149.pdf}, }

Contact and feedback

We appreciate your feedback! If you have any comments, suggestions, or run into issues while using VueGen, feel free to open an issue in this repository. Your input helps us make VueGen better for everyone.