期刊
JOURNAL OF PROTEOME RESEARCH
卷 19, 期 1, 页码 537-542出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.9b00328
关键词
bioinformatics; file formats; open source; cloud; mass spectrometry; software; big data; workflows; mzML; metadata
资金
- European Commission within the Research Infrastructures programme of Horizon 2020 [676559]
- FWO [3G0H6916, 604814, G042518]
- Wellcome Trust [208391/Z/17/Z]
- Horizon 2020 programme of the European Union [823839]
- BBSRC [BB/P024599/1]
- Bergen Research Foundation
- Research Council of Norway
- German Federal Ministry of Education and Research (BMBF) [031A535A]
- ELIXIR Implementation Study
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.
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