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Massdash

MassDash: A web-based dashboard for streamlined DIA-MS visualization, analysis, prototyping, and optimization

Install / Use

/learn @Roestlab/Massdash
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://github.com/Roestlab/massdash/raw/dev/massdash/assets/img/MassDash_Logo_Light.png" alt="MassDash_Logo" width="500"> <source media="(prefers-color-scheme: light)" srcset="https://github.com/Roestlab/massdash/raw/dev/massdash/assets/img/MassDash_Logo_Dark.png" alt="MassDash_Logo" width="500"> <img alt="MassDash Logo" comment="Placeholder to transition between light color mode and dark color mode - this image is not directly used." src="https://github.com/Roestlab/massdash/raw/dev/massdash/assets/img/MassDash_Logo_Dark.png"> </picture> </p>

pypipv pypiv pypidownload

<!-- [![biocondav](https://img.shields.io/conda/v/bioconda/massdash?label=bioconda&color=purple)](https://anaconda.org/bioconda/massdash) -->

dockerv dockerpull continuous-integration demoapp readthedocs Licence

MassDash is a visualization and data exploration platform for Data-Independent Acquisition mass spectrometry data.

Key Features Include:

  • Chromatogram Visualization - Easily view and analyze chromatograms for an in-depth examination of peptide precursors of interest.
  • 2D and 3D Visualizations - Visualization of ion mobility enhacanced mass spectrometry and other 2D and 3D plots.
  • On the fly parameter optimization - Adjust peak picking parameters on the fly or experiment with novel deep learning based peak picking approaches.
  • Algorithm testing - Develop and fine-tune custom algorithms by interfacing with MassDash's various data analysis algorithms and workflows.
  • Usage Flexibility - User-friendly web based dashboard for quick visualizations, advanced python package for more complex applications

One Click Installation

installwindows installmacos installubuntu demoapp

For a one-click installation, click on the corresponding badge corresponding to your operating system, or visit the latest release page and download the installer for your operating system.

(Recommended) Pip Installation

The recommended way of installing MassDash is through the Python Package Index (PyPI). We recommend installing MassDash in its own virtual environment using Anaconda to avoid packaging conflicts.

First create a new environemnt:

conda create --name=massdash python=3.9
conda activate massdash 

Then in the new environment install MassDash.

pip install massdash --upgrade

After installation the GUI can be launched in the Terminal/Anaconda Prompt using

massdash gui
<!-- or, install the latest stable version of MassDash from Bioconda if you are using Anaconda for package and environment management: ```bash conda install bioconda::massdash --upgrade ``` -->

For detailed OS-specific (Windows, MacOS, Ubuntu) installation guides, please refer to the documentation.

GUI Quick start

Launch MassDash by typing the following command in your terminal/Anaconda Prompt:

massdash gui

For more information on the GUI, please refer to the documentation.

<p align="left"> <img alt="MassDash Landing Page" style="width: 80%;" src="https://github.com/Roestlab/massdash/raw/dev/massdash/assets/img/MassDash_Landing_Page.png"> </p>

Demo

To run a demo version of MassDash, you can visit the streamlit cloud hosted demo version here. Note that full functionality is not avaliable in the demo app.

Documentation

For more information (API and tutorial walk-throughs), please refer to the documentation.

Contribute

Support

If you are having issues or would like to propose a new feature, please use the issues tracker.

License

This project is licensed under the BSD 3-Clause license.

Citation

Sing JC, Charkow J, AlHigaylan M, Horecka I, Xu L, Röst HL. MassDash: A Web-Based Dashboard for Data-Independent Acquisition Mass Spectrometry Visualization. J Proteome Res. 2024 Jun 7;23(6):2306-2314. doi: 10.1021/acs.jproteome.4c00026. Epub 2024 Apr 29.

Related Skills

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GitHub Stars22
CategoryProduct
Updated12d ago
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Languages

HTML

Security Score

95/100

Audited on Mar 29, 2026

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