Open Mass Spectrometry Search Algorithm. Geer, L. Y., Markey, S. P., Kowalak, J. A., Wagner, L., Xu, M., Maynard, D. M., Yang, X., Shi, W., & Bryant, S. H. J Proteome Res, 3(5):958–964, 2004.
doi  abstract   bibtex   
Large numbers of MS/MS peptide spectra generated in proteomics experiments require efficient, sensitive and specific algorithms for peptide identification. In the Open Mass Spectrometry Search Algorithm (OMSSA), specificity is calculated by a classic probability score using an explicit model for matching experimental spectra to sequences. At default thresholds, OMSSA matches more spectra from a standard protein cocktail than a comparable algorithm. OMSSA is designed to be faster than published algorithms in searching large MS/MS datasets.
@Article{geer04open,
  author    = {Lewis Y. Geer and Sanford P. Markey and Jeffrey A. Kowalak and Lukas Wagner and Ming Xu and Dawn M. Maynard and Xiaoyu Yang and Wenyao Shi and Stephen H. Bryant},
  title     = {Open Mass Spectrometry Search Algorithm},
  journal   = {J Proteome Res},
  year      = {2004},
  volume    = {3},
  number    = {5},
  pages     = {958--964},
  abstract  = {Large numbers of MS/MS peptide spectra generated in proteomics experiments require efficient, sensitive and specific algorithms for peptide identification. In the Open Mass Spectrometry Search Algorithm (OMSSA), specificity is calculated by a classic probability score using an explicit model for matching experimental spectra to sequences. At default thresholds, OMSSA matches more spectra from a standard protein cocktail than a comparable algorithm. OMSSA is designed to be faster than published algorithms in searching large MS/MS datasets.},
  comment   = {OMSSA},
  doi       = {10.1002/pmic.200600273},
  file      = {GeerEtAl_OpenMassSpectrometry_JPR_2004.pdf:2004/GeerEtAl_OpenMassSpectrometry_JPR_2004.pdf:PDF},
  keywords  = {MS; Mass Spectrometry; computational MS; proteomics; peptides;},
  owner     = {apervukh},
  pmid      = {15473683},
  timestamp = {2009.06.04},
}
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