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@ARTICLE{Leufken:128689,
      author       = {J. Leufken and A. Niehues and L. P. Sarin and F. Wessel$^*$
                      and M. Hippler and S. A. Leidel and C. Fufezan},
      title        = {py{Q}ms enables universal and accurate quantification of
                      mass spectrometry data.},
      journal      = {Molecular $\&$ cellular proteomics},
      volume       = {16},
      number       = {10},
      issn         = {1535-9484},
      address      = {Bethesda, Md.},
      publisher    = {The American Society for Biochemistry and Molecular
                      Biology},
      reportid     = {DKFZ-2017-04704},
      pages        = {1736 - 1745},
      year         = {2017},
      abstract     = {Quantitative mass spectrometry (MS) is a key technique in
                      many research areas (1), including proteomics, metabolomics,
                      glycomics, and lipidomics. Because all of the corresponding
                      molecules can be described by chemical formulas, universal
                      quantification tools are highly desirable. Here, we present
                      pyQms, an open-source software for accurate quantification
                      of all types of molecules measurable by MS. pyQms uses
                      isotope pattern matching that offers an accurate quality
                      assessment of all quantifications and the ability to
                      directly incorporate mass spectrometer accuracy. pyQms is,
                      due to its universal design, applicable to every research
                      field, labeling strategy, and acquisition technique. This
                      opens ultimate flexibility for researchers to design
                      experiments employing innovative and hitherto unexplored
                      labeling strategies. Importantly, pyQms performs very well
                      to accurately quantify partially labeled proteomes in large
                      scale and high throughput, the most challenging task for a
                      quantification algorithm.},
      cin          = {G181},
      ddc          = {540},
      cid          = {I:(DE-He78)G181-20160331},
      pnm          = {317 - Translational cancer research (POF3-317)},
      pid          = {G:(DE-HGF)POF3-317},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:28729385},
      pmc          = {pmc:PMC5629261},
      doi          = {10.1074/mcp.M117.068007},
      url          = {https://inrepo02.dkfz.de/record/128689},
}