library.bib

@article{lamBuildingConsensusSpectral2008,
  title = {Building Consensus Spectral Libraries for Peptide Identification in Proteomics},
  author = {Lam, Henry and Deutsch, Eric W. and Eddes, James S. and Eng, Jimmy K. and Stein, Stephen E. and Aebersold, Ruedi},
  date = {2008-10},
  journaltitle = {Nature Methods},
  shortjournal = {Nat Methods},
  volume = {5},
  pages = {873--875},
  issn = {1548-7105},
  doi = {10.1038/nmeth.1254},
  url = {https://www.nature.com/articles/nmeth.1254},
  urldate = {2020-02-06},
  abstract = {Spectral searching, based on matching experimental peptide spectra to reference spectral libraries, is gaining interest as an alternative to traditional sequence-database searching in mass spectrometry–based proteomics. A software tool, SpectraST, now allows users to build their own high-quality spectral libraries from raw data.},
  file = {C\:\\Users\\mbpssmr9.DS\\Zotero\\storage\\KA6DU582\\Lam et al. - 2008 - Building consensus spectral libraries for peptide .pdf;C\:\\Users\\mbpssmr9.DS\\Zotero\\storage\\LL5RBFFS\\nmeth.html},
  langid = {english},
  number = {10}
}



@article{reiterProteinIdentificationFalse2009,
  ids = {reiterProteinIdentificationFalse2009a},
  title = {Protein {{Identification False Discovery Rates}} for {{Very Large Proteomics Data Sets Generated}} by {{Tandem Mass Spectrometry}}},
  author = {Reiter, Lukas and Claassen, Manfred and Schrimpf, Sabine P. and Jovanovic, Marko and Schmidt, Alexander and Buhmann, Joachim M. and Hengartner, Michael O. and Aebersold, Ruedi},
  year = {2009},
  month = nov,
  volume = {8},
  pages = {2405--2417},
  issn = {1535-9476, 1535-9484},
  doi = {10.1074/mcp.M900317-MCP200},
  abstract = {Comprehensive characterization of a proteome is a fundamental goal in proteomics. To achieve saturation coverage of a proteome or specific subproteome via tandem mass spectrometric identification of tryptic protein sample digests, proteomics data sets are growing dramatically in size and heterogeneity. The trend toward very large integrated data sets poses so far unsolved challenges to control the uncertainty of protein identifications going beyond well established confidence measures for peptide-spectrum matches. We present MAYU, a novel strategy that reliably estimates false discovery rates for protein identifications in large scale data sets. We validated and applied MAYU using various large proteomics data sets. The data show that the size of the data set has an important and previously underestimated impact on the reliability of protein identifications. We particularly found that protein false discovery rates are significantly elevated compared with those of peptide-spectrum matches. The function provided by MAYU is critical to control the quality of proteome data repositories and thereby to enhance any study relying on these data sources. The MAYU software is available as standalone software and also integrated into the Trans-Proteomic Pipeline.},
  copyright = {\textcopyright{} 2009 by The American Society for Biochemistry and Molecular Biology, Inc.},
  file = {C\:\\Users\\mbpssmr9.DS\\Zotero\\storage\\7V39CH2P\\Reiter et al. - 2009 - Protein Identification False Discovery Rates for V.pdf;C\:\\Users\\mbpssmr9.DS\\Zotero\\storage\\KCRS4KE5\\Reiter et al. - 2009 - Protein Identification False Discovery Rates for V.pdf;C\:\\Users\\mbpssmr9.DS\\Zotero\\storage\\HGF3HV3B\\2405.html;C\:\\Users\\mbpssmr9.DS\\Zotero\\storage\\QB7DVJWR\\2405.html},
  journal = {Molecular \& Cellular Proteomics},
  language = {en},
  number = {11},
  pmid = {19608599}
}