MassDash: A Web-based Dashboard for Data-Independent Acquisition Mass Spectrometry Visualization

Abstract With the increased usage, diversity of methods and instruments being applied to analyze Data-Independent Acquisition (DIA) data, visualization is becoming increasingly important to validate automated software results. Here we present MassDash, a cross-platform, DIA mass spectrometry visualization and validation software for comparing features and results across popular tools. MassDash provides a web-based interface and Python package for interactive feature visualizations and summary report plots across multiple automated DIA feature detection tools including OpenSwath, DIA-NN, and dreamDIA. Furthermore, MassDash processes peptides on the fly, enabling interactive visualization of peptides across dozens of runs simultaneously on a personal computer. MassDash supports various multidimensional visualizations across retention time, ion mobility, m/z, and intensity providing additional insights into the data. The modular framework is easily extendable enabling rapid algorithm development of novel peak picker techniques, such as deep learning based approaches and refinement of existing tools. MassDash is open-source under a BSD 3-Clause license and freely available at https://github.com/Roestlab/massdash, and a demo version can be accessed at https://massdash.streamlit.app. Competing Interest Statement The authors have declared no competing interest. Footnotes In response to the valuable feedback provided by the reviewers, several enhancements and clarifications have been incorporated into the manuscript. The major revisions and updates include: 1. Platform Accessibility: Addressed installation issues on Windows by extending the documentation and providing one-click installers. A demo version of MassDash has been deployed via Streamlit Cloud for easier access. 2. Title Clarity: The title has been refined to accurately reflect the platform's focus on data-independent acquisition mass spectrometry. 3. Concise Method Section: The Method section has been streamlined to focus on specific modules, parameters, and methodologies relevant to the study. 4. Dataset Clarity: Clarified the nature of the 16 runs used in the study to distinguish between distinct samples and provided additional details on dataset origins. 5. Data Presentation: Reduced the presentation of miscellaneous data and emphasized comprehensive and in-depth data presentations on the MassDash platform. 6. Memory and Time Efficiency: Provided specific numerical values to demonstrate MassDash's low-memory data capability and execution time across different input types. 7. Tool Functionality: Clarified the functionality of MassDash as a data exploration, visualization, and validation tool, emphasizing its role in validating search result tools and optimizing parameters. 8. Abbreviation Usage: Ensured consistent usage and definition of abbreviations throughout the manuscript to avoid confusion. 9. Figure Improvements: Enhanced figure readability by increasing resolution, removing legends, and adjusting font sizes. 10. Web Address Inclusion: Provided the GitHub repository URL and a Streamlit hosted demo version URL for easy access to MassDash. These revisions aim to improve the clarity, accessibility, and usability of the MassDash platform for researchers in the field of mass spectrometry analysis.