The HUPO-PSI standardized spectral library format

More and more proteomics datasets are becoming available in public repositories. The knowledge embedded in these datasets can be used to improve peptide identification workflows. Spectral library searching provides a straightforward method to boost identification rates using previously identified spectra. Alternatively, machine learning methods can learn from these spectra to accurately predict the behavior of peptides in a liquid chromatography-mass spectrometry system. At the basis of both approaches are spectral libraries: Unified collections of previously identified spectra. Organizations and projects such as the National Institute of Standards and Technology (NIST), the Global Proteome Machine, PeptideAtlas, PRIDE Archive and MassIVE have all compiled spectral libraries for a multitude of species and experimental setups. A large obstacle, however, is that each organization provides libraries in a different file format. At the software level the problem propagates (if not expands), as different software tools require different file formats. The solution is a standardized spectral library format that is sufficiently flexible to meet all users' demands, but that is also standardized enough to be usable across environments and software packages. This balance is achieved by setting up a standardized framework and a controlled vocabulary with metadata terms, and allow the format to be represented in different forms, such as plain text, JSON and HDF. So far, the required (and optional) meta data has been compiled and added to the PSI-MS ontology, and versions of the text and JSON representations have been drafted. The tabular and HDF representations of the format are in development, as well as converters and validators in various programming languages

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

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

ThermoRawFileParser : modular, scalable and cross-platform RAW file conversion

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

TraML: a standard format for exchange of selected reaction monitoring transition lists

Targeted proteomics via selected reaction monitoring (SRM) is a powerful mass spectrometric technique affording higher dynamic range, increased specificity and lower limits of detection than other shotgun mass spectrometry methods when applied to proteome analyses. However, it involves selective measurement of predetermined analytes, which requires more preparation in the form of selecting appropriate signatures for the proteins and peptides that are to be targeted. There is a growing number of software programs and resources for selecting optimal transitions and the instrument settings used for the detection and quantification of the targeted peptides, but the exchange of this information is hindered by a lack of a standard format. We have developed a new standardized format, called TraML, for encoding transition lists and associated metadata. In addition to introducing the TraML format, we demonstrate several implementations across the community, and provide semantic validators, extensive documentation, and multiple example instances to demonstrate correctly written documents. Widespread use of TraML will facilitate the exchange of transitions, reduce time spent handling incompatible list formats, increase the reusability of previously optimized transitions, and thus accelerate the widespread adoption of targeted proteomics via SRM

The HUPO-PSI standardized spectral library format

More and more proteomics datasets are becoming available in public repositories. The knowledge embedded in these datasets can be used to improve peptide identification workflows. Spectral library searching provides a straightforward method to boost identification rates using previously identified spectra. Alternatively, machine learning methods can learn from these spectra to accurately predict the behavior of peptides in a liquid chromatography-mass spectrometry system. At the basis of both approaches are spectral libraries: Unified collections of previously identified spectra. Organizations and projects such as the National Institute of Standards and Technology (NIST), the Global Proteome Machine, PeptideAtlas, PRIDE Archive and MassIVE have all compiled spectral libraries for a multitude of species and experimental setups. A large obstacle, however, is that each organization provides libraries in a different file format. At the software level the problem propagates (if not expands), as different software tools require different file formats. The solution is a standardized spectral library format that is sufficiently flexible to meet all users' demands, but that is also standardized enough to be usable across environments and software packages. This balance is achieved by setting up a standardized framework and a controlled vocabulary with metadata terms, and allow the format to be represented in different forms, such as plain text, JSON and HDF. So far, the required (and optional) meta data has been compiled and added to the PSI-MS ontology, and versions of the text and JSON representations have been drafted. The tabular and HDF representations of the format are in development, as well as converters and validators in various programming languages

Universal Spectrum Identifier for mass spectra

Mass spectra provide the ultimate evidence to support the findings of mass spectrometry proteomics studies in publications, and it is therefore crucial to be able to trace the conclusions back to the spectra. The Universal Spectrum Identifier (USI) provides a standardized mechanism for encoding a virtual path to any mass spectrum contained in datasets deposited to public proteomics repositories. USI enables greater transparency of spectral evidence, with more than 1 billion USI identifications from over 3 billion spectra already available through ProteomeXchange repositories

Universal Spectrum Identifier for mass spectra.

Mass spectra provide the ultimate evidence to support the findings of mass spectrometry proteomics studies in publications, and it is therefore crucial to be able to trace the conclusions back to the spectra. The Universal Spectrum Identifier (USI) provides a standardized mechanism for encoding a virtual path to any mass spectrum contained in datasets deposited to public proteomics repositories. USI enables greater transparency of spectral evidence, with more than 1 billion USI identifications from over 3 billion spectra already available through ProteomeXchange repositories