AI Article Synopsis
- Modern mass spectrometry techniques generate complex, multidimensional data that can enhance our understanding of omics studies when paired with artificial intelligence, but current data storage solutions struggle with the volume of this data.
- MZA is a new mass-to-charge data storage and access tool designed to overcome these storage challenges, encouraging development in data processing and AI for mass spectrometry.
- It utilizes an easy-to-use file structure based on HDF5, allowing quick access to raw mass spectrometry data from different programming languages like Python and R, with all necessary resources available for free online.
Article Abstract
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 ), the mass-to-charge (/) 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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9898216 | PMC |
| http://dx.doi.org/10.1021/acs.jproteome.2c00313 | DOI Listing |
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