Open AccessThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion
Author(s) -
Niels Hulstaert,
Jim Shofstahl,
Timo Sachsenberg,
Mathias Walzer,
Harald Barsnes,
Lennart Martens,
Yasset PerezRiverol
Publication year - 2019
Publication title -
journal of proteome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.644
H-Index - 161
eISSN - 1535-3907
pISSN - 1535-3893
DOI - 10.1021/acs.jproteome.9b00328
Subject(s) - computer science , workflow , modular design , scalability , file format , operating system , benchmark (surveying) , container (type theory) , interface (matter) , cloud computing , database , open source , software , engineering , mechanical engineering , geodesy , bubble , maximum bubble pressure method , geography
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.