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

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 challeng...

Full description

Saved in:
Bibliographic Details
Published in:Journal of proteome research 2023-02, Vol.22 (2), p.508-513
Main Authors: Bilbao, Aivett, Ross, Dylan H., Lee, Joon-Yong, Donor, Micah T., Williams, Sarah M., Zhu, Ying, Ibrahim, Yehia M., Smith, Richard D., Zheng, Xueyun
Format: Article
Language:English
Subjects:
Artificial Intelligence
data conversion
data-independent acquisition
Information Storage and Retrieval
INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
ion mobility spectrometry
m/q Initiative
mass spectrometry
Mass Spectrometry - methods
open data format
Programming Languages
Software
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1535-3893
1535-3907
DOI:10.1021/acs.jproteome.2c00313