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Technical Note: mzML and imzML Libraries for Processing Mass Spectrometry Data with the High-Performance Programming
Ignacio Rosas-Román1, Héctor Guillén-Alonso2,3, Abigail Moreno-Pedraza4,5
1Universidad de Guanajuato, División de Ciencias e Ingenierías, Loma del Bosque 103, Lomas del Campestre, 37150 León, Guanajuato, Mexico.
Analytical Chemistry
|March 1, 2024
Summary
A new Julia library enables reading mass spectrometry (MS) data in mzML and imzML formats. This significantly speeds up data processing for large-scale MS-Omics and MS imaging, overcoming previous limitations.
Area of Science:
- Computational Biology
- Data Science
- Analytical Chemistry
Background:
- Julia programming language offers high-level readability and low-level performance.
- Existing Julia libraries lacked support for importing mass spectrometry (MS) data.
- This created a bottleneck for computationally intensive MS data analysis.
Purpose of the Study:
- To develop a Julia library for importing mass spectrometry data in HUPO standard mzML and imzML formats.
- To demonstrate the library's functionality across various MS techniques and platforms.
- To improve the efficiency of large-scale MS data processing workflows.
Main Methods:
- Developed a Julia library for parsing mzML and imzML file formats.
- Tested the library with direct/ambient ionization MS, liquid chromatography-MS, and MS imaging data.
- Compared processing speed against R for imzML MS imaging files.
Main Results:
- The Julia library successfully imports MS data in mzML and imzML formats.
- Processing speed for imzML MS imaging files in Julia was up to 214 times faster than R.
- The library functions across Windows, Linux, and Mac OS.
Conclusions:
- Julia can now efficiently process large-scale mass spectrometry data, including MS-Omics and MS imaging.
- This library removes computational bottlenecks, enabling real-time monitoring and complex pattern recognition.
- The developed libraries and code are available under the MIT license for broader adoption.
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