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MZA: A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry

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    MZA: A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry
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    Journal of Proteome Research

    Cite this: J. Proteome Res. 2023, 22, 2, 508–513
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    https://doi.org/10.1021/acs.jproteome.2c00313
    Published November 22, 2022
    Copyright © 2022 American Chemical Society
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    Abstract

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    Modern mass spectrometry-based workflows employing hybrid instrumentation and orthogonal separations collect multidimensional data, potentially allowing deeper understanding in omics studies through adoption of artificial intelligence methods. However, the large volume of these rich spectra challenges existing data storage and access technologies, therefore precluding informatics advancements. We present MZA (pronounced m-za), the mass-to-charge (m/z) generic data storage and access tool designed to facilitate software development and artificial intelligence research in multidimensional mass spectrometry measurements. Composed of a data conversion tool and a simple file structure based on the HDF5 format, MZA provides easy, cross-platform and cross-programming language access to raw MS-data, enabling fast development of new tools in data science programming languages such as Python and R. The software executable, example MS-data and example Python and R scripts are freely available at https://github.com/PNNL-m-q/mza.

    Copyright © 2022 American Chemical Society

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    Cited By

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    This article is cited by 6 publications.

    1. Andrea Harrison, Josie G. Eder, Priscila M. Lalli, Nathalie Munoz, Yuqian Gao, Chaevien S. Clendinen, Daniel J. Orton, Xueyun Zheng, Sarah M. Williams, Sneha P. Couvillion, Rosalie K. Chu, Vimal K. Balasubramanian, Arunima Bhattacharjee, Christopher R. Anderton, Kyle R. Pomraning, Kristin E. Burnum-Johnson, Tao Liu, Jennifer E. Kyle, Aivett Bilbao. PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data. Journal of the American Society for Mass Spectrometry 2024, 35 (11) , 2680-2689. https://doi.org/10.1021/jasms.4c00146
    2. Dylan Ross, Aivett Bilbao, Joon-Yong Lee, Xueyun Zheng. mzapy: An Open-Source Python Library Enabling Efficient Extraction and Processing of Ion Mobility Spectrometry-Mass Spectrometry Data in the MZA File Format. Analytical Chemistry 2023, 95 (25) , 9428-9431. https://doi.org/10.1021/acs.analchem.3c01653
    3. Dylan H. Ross, Erin L. Bredeweg, Josie G. Eder, Daniel J. Orton, Meagan C. Burnet, Jennifer E. Kyle, Ernesto S. Nakayasu, Xueyun Zheng. A deep learning‐guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis. Journal of Mass Spectrometry 2024, 59 (9) https://doi.org/10.1002/jms.5078
    4. Bradley J. Smith, Paul C. Guest, Daniel Martins-de-Souza. Maximizing Analytical Performance in Biomolecular Discovery with LC-MS: Focus on Psychiatric Disorders. Annual Review of Analytical Chemistry 2024, 17 (1) , 25-46. https://doi.org/10.1146/annurev-anchem-061522-041154
    5. Dylan H. Ross, Harsh Bhotika, Xueyun Zheng, Richard D. Smith, Kristin E. Burnum‐Johnson, Aivett Bilbao. Computational tools and algorithms for ion mobility spectrometry‐mass spectrometry. PROTEOMICS 2024, 24 (12-13) https://doi.org/10.1002/pmic.202200436
    6. Dylan H. Ross, Aivett Bilbao, Richard D. Smith, Xueyun Zheng. Ion Mobility Spectrometry‐Mass Spectrometry for High‐Throughput Analysis. 2023, 183-213. https://doi.org/10.1002/9781119678496.ch6
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    Journal of Proteome Research

    Cite this: J. Proteome Res. 2023, 22, 2, 508–513
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jproteome.2c00313
    Published November 22, 2022
    Copyright © 2022 American Chemical Society
    Request reuse permissions

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