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J Proteome Res. 2020 Jan 3;19(1):537-542. doi: 10.1021/acs.jproteome.9b00328. Epub 2019 Dec 6.

ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion.

Author information

1
VIB-UGent Center for Medical Biotechnology, VIB , Ghent B-9000 , Belgium.
2
Department of Biomolecular Medicine , Ghent University , Ghent B-9000 , Belgium.
3
Thermo Fisher Scientific , 355 River Oaks Parkway , San Jose , California 95134 , United States.
4
Applied Bioinformatics, Department for Computer Science , University of Tuebingen , Sand 14 , 72076 Tuebingen , Germany.
5
European Molecular Biology Laboratory , European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD , United Kingdom.
6
Computational Biology Unit (CBU), Department of Informatics , University of Bergen , Bergen 5020 , Norway.
7
Proteomics Unit (PROBE), Department of Biomedicine , University of Bergen , Bergen 5020 , Norway.

Abstract

The field of computational proteomics is approaching the big data age, driven both by a continuous growth in the number of samples analyzed per experiment as well as by the growing amount of data obtained in each analytical run. In order to process these large amounts of data, it is increasingly necessary to use elastic compute resources such as Linux-based cluster environments and cloud infrastructures. Unfortunately, the vast majority of cross-platform proteomics tools are not able to operate directly on the proprietary formats generated by the diverse mass spectrometers. Here, we present ThermoRawFileParser, an open-source, cross-platform tool that converts Thermo RAW files into open file formats such as MGF and the HUPO-PSI standard file format mzML. To ensure the broadest possible availability and to increase integration capabilities with popular workflow systems such as Galaxy or Nextflow, we have also built Conda package and BioContainers container around ThermoRawFileParser. In addition, we implemented a user-friendly interface (ThermoRawFileParserGUI) for those users not familiar with command-line tools. Finally, we performed a benchmark of ThermoRawFileParser and msconvert to verify that the converted mzML files contain reliable quantitative results.

KEYWORDS:

big data; bioinformatics; cloud; file formats; mass spectrometry; metadata; mzML; open source; software; workflows

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