MZA: A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry
- Aivett Bilbao*
- Dylan H. RossDylan H. RossPacific Northwest National Laboratory, Richland, Washington 99352, United StatesMore by Dylan H. Ross
- Joon-Yong Lee
- Micah T. DonorMicah T. DonorPacific Northwest National Laboratory, Richland, Washington 99352, United StatesMore by Micah T. Donor
- Sarah M. WilliamsSarah M. WilliamsPacific Northwest National Laboratory, Richland, Washington 99352, United StatesMore by Sarah M. Williams
- Ying Zhu
- Yehia M. Ibrahim
- Richard D. SmithRichard D. SmithPacific Northwest National Laboratory, Richland, Washington 99352, United StatesMore by Richard D. Smith
- , and
- Xueyun Zheng*
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.
This article is cited by 1 publications.
- 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