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@ARTICLE{Hoffmann:910686,
      author       = {Hoffmann, Nils and Mayer, Gerhard and Has, Canan and
                      Kopczynski, Dominik and Al Machot, Fadi and Schwudke,
                      Dominik and Ahrends, Robert and Marcus, Katrin and
                      Eisenacher, Martin and Turewicz, Michael},
      title        = {{A} {C}urrent {E}ncyclopedia of {B}ioinformatics {T}ools,
                      {D}ata {F}ormats and {R}esources for {M}ass {S}pectrometry
                      {L}ipidomics},
      journal      = {Metabolites},
      volume       = {12},
      number       = {7},
      issn         = {2218-1989},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2022-04058},
      pages        = {584 -},
      year         = {2022},
      abstract     = {Mass spectrometry is a widely used technology to identify
                      and quantify biomolecules such as lipids, metabolites and
                      proteins necessary for biomedical research. In this study,
                      we catalogued freely available software tools, libraries,
                      databases, repositories and resources that support
                      lipidomics data analysis and determined the scope of
                      currently used analytical technologies. Because of the
                      tremendous importance of data interoperability, we assessed
                      the support of standardized data formats in mass
                      spectrometric (MS)-based lipidomics workflows. We included
                      tools in our comparison that support targeted as well as
                      untargeted analysis using direct infusion/shotgun (DI-MS),
                      liquid chromatography−mass spectrometry, ion mobility or
                      MS imaging approaches on MS1 and potentially higher MS
                      levels. As a result, we determined that the Human Proteome
                      Organization-Proteomics Standards Initiative standard data
                      formats, mzML and mzTab-M, are already supported by a
                      substantial number of recent software tools. We further
                      discuss how mzTab-M can serve as a bridge between data
                      acquisition and lipid bioinformatics tools for
                      interpretation, capturing their output and transmitting rich
                      annotated data for downstream processing. However, we
                      identified several challenges of currently available tools
                      and standards. Potential areas for improvement were:
                      adaptation of common nomenclature and standardized reporting
                      to enable high throughput lipidomics and improve its data
                      handling. Finally, we suggest specific areas where tools and
                      repositories need to improve to become FAIRer.},
      cin          = {IBG-5},
      ddc          = {540},
      cid          = {I:(DE-Juel1)IBG-5-20220217},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35888710},
      UT           = {WOS:000831792900001},
      doi          = {10.3390/metabo12070584},
      url          = {https://juser.fz-juelich.de/record/910686},
}