mzML and imzML Libraries for Processing Mass Spectrometry Data with the High-Performance Programming Language Julia
Artículo
Te invitamos a leer el artículo "mzML and imzML Libraries for Processing Mass Spectrometry Data with the High-Performance Programming Language Julia" publicado en Analytical Chemistry, a cargo del profesor investigador Dr. Robert Winkler y su equipo de trabajo de la UGA-Langebio.
Autores: Ignacio Rosas-Román, Héctor Guillén-Alonso, Abigail Moreno-Pedraza, and Robert Winkler*
- Universidad de Guanajuato, División de Ciencias e Ingenierías, León, Guanajuato, Mexico
- Center for Research and Advanced Studies (CINVESTAV) Irapuato, UGA-Langebio, Irapuato, Guanajuato, Mexico
- Department of Biochemical Engineering, National Technological Institute, Celaya, Guanajuato, Mexico
- Institute for Vegetable and Ornamental Crops (IGZ) e.V., Theodor-Echtermeyer-Weg 1, 14979, Großbeeren, Germany
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger-Str. 159, 07743, Jena, Germany
- Center for Research and Advanced Studies (CINVESTAV) Irapuato, UGA-Langebio, Irapuato, Guanajuato, Mexico
Felicitamos al estudiantado y profesorado que contribuyeron en esta investigación por su arduo trabajo.
Summary:
Julia combines the virtues of high-level and low-level programming languages: The code is human-readable, and the performance of the created binaries competes with machine-orientated compilers. Thus, Julia is popular in “Big Data” sciences. Reading mass spectrometry (MS) data with Julia was impossible until now due to missing libraries. Here, we present a Julia library for importing mass spectrometry (MS) data in HUPO standard mzML and imzML formats and demonstrate its function with direct and ambient ionization MS, liquid chromatography-MS, and MS imaging data on standard platforms (Windows, Linux, and Mac OS). The processing speed of Julia for reading imzML MS imaging files was up to 214 times faster than the comparable code in R. Julia can remove bottlenecks for computationally demanding tasks in large-scale MS-Omics and MS imaging data processing workflows and supports their agile development. In addition, time-critical and complex data evaluation tasks become possible, such as following the real-time monitoring of biological processes and pattern recognition in large MS imaging projects. Our mzML/imzML libraries and code examples are available under the terms of the MIT license from https://github.com/CINVESTAV-LABI/julia_mzML_imzML.