MassDash: a web-based dashboard for data-independent acquisition mass spectrometry visualization

Introduction:

In bottom-up mass spectrometry-based proteomics, Data Independent Acquisition (DIA) is a robust strategy for reproducibly quantifying the proteome. Due to its popularity, DIA methods continue to be refined and applied to new instruments and applications. Even though DIA proteomics is usually analyzed with automated software solutions, considering the vast set of applications, manual inspection of identified peptide features is necessary to ensure that algorithms are functioning as expected. Although well-established visualization tools exist, many are platform-specific or limited to one automated analysis tool which prevents comparison between tools. We sought to create a cross-platform, easy-to-use, and extendable framework for DIA proteomics visualization tool to be used post-automated analysis. Here, we present MassDash, a Python package, and Web-based interactive viewer, which currently supports OpenSwath, DIA-NN, and dreamDIA. MassDash is designed for tool comparison, validation, parameter optimization, and novel method development.

Methods:

MassDash is implemented as a Python package with a web-based GUI. MassDash ships with various feature detection algorithms including the OpenSwath and the Conformer deep-learning peak pickers. Interactive plotting is built using bokeh and plotly. MassDash has been tested with numerous datasets including, the SWATH-MS gold standard dataset, a diaPASEF dataset, and a synthetic-phospho dataset.

Results:

Using a raw mass spectrometry file and its corresponding identification results from OpenSwath, DIA-NN, or dreamDIA, MassDash allows for visualization of any given peptide precursor and the reported feature boundaries. MassDash supports various visualization options including 2D heatmap visualization across retention time and ion mobility and 3D visualization plots. Furthermore, users can also compare peptide quantification boundaries across different runs and software tools. Visual inspection of several peptide precursor examples identified by OpenSwath and DIA-NN demonstrates that peptide precursors come in varying quality and disagreement between software tools can occur with low-quality features. Moreover, MassDash can be used for realtime visualization of peak picking on the extracted ion chromatograms. Current built-in methods allow for on-the-fly parameter optimization which could then be used to reanalyze the experiment.

Conclusions:

MassDash is a versatile application supporting a on-the-fly feature detection and a variety of visualizations that can be used for parameter optimization and validation of results of multiple software tools. MassDash is open-source under a BSD 3-Clause license and can be installed at https://github.com/Roestlab/massdash. More information can be found in our recent publication (DOI: 10.1021/acs.jproteome.4c00026). A demo version of MassDash can be accessed at https://massdash.streamlit.app.