title: Assigning spectrum-specific P-values to protein identifications by mass spectrometry.
abstract: BACKGROUND
  Although many methods and statistical approaches have been developed for protein identification by mass spectrometry, the problem of accurate assessment of statistical significance of protein identifications remains an open question. The main issues are as follows: (i) statistical significance of inferring peptide from experimental mass spectra must be platform independent and spectrum specific and (ii) individual spectrum matches at the peptide level must be combined into a single statistical measure at the protein level.
  
  
  RESULTS
  We present a method and software to assign statistical significance to protein identifications from search engines for mass spectrometric data. The approach is based on asymptotic theory of order statistics. The parameters of the asymptotic distributions of identification scores are estimated for each spectrum individually. The method relies on new unbiased estimators for parameters of extreme value distribution. The estimated parameters are used to assign a spectrum-specific P-value to each peptide-spectrum match. The protein-level confidence measure combines P-values of peptide-to-spectrum matches.
  
  
  CONCLUSIONS
  We extensively tested the method using triplicate mouse and yeast high-throughput proteomic experiments. The proposed statistical approach improves the sensitivity of protein identifications without compromising specificity. While the method was primarily designed to work with Mascot, it is platform-independent and is applicable to any search engine which outputs a single score for a peptide-spectrum match. We demonstrate this by testing the method in conjunction with X!Tandem.
  
  
  BACKGROUND
  The software is available for download at ftp://genetics.bwh.harvard.edu/SSPV/.
  
  
  BACKGROUND
  ssunyaev@rics.bwh.harvard.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Division of Genetics, Brigham and Women's Hospital, Department of Cell Biology, Harvard Medical School, 240 Longwood Avenue, Boston, MA 02115, USA.
authors.forename: Victor
authors.initials: V
authors.lastname: Spirin
authors.forename: Alexander
authors.initials: A
authors.lastname: Shpunt
authors.forename: Jan
authors.initials: J
authors.lastname: Seebacher
authors.forename: Marc
authors.initials: M
authors.lastname: Gentzel
authors.forename: Andrej
authors.initials: A
authors.lastname: Shevchenko
authors.forename: Steven
authors.initials: S
authors.lastname: Gygi
authors.forename: Shamil
authors.initials: S
authors.lastname: Sunyaev
mesh_terms: D000465:Algorithms; D000818:Animals; D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D013058:Mass Spectrometry / Q000379:methods*; D051379:Mice; D010455:Peptides / Q000737:chemistry; D011506:Proteins / Q000032:analysis / Q000737:chemistry*; D040901:Proteomics; D029701:Saccharomyces cerevisiae Proteins / Q000032:analysis / Q000737:chemistry; D012984:Software
chemical_list: D010455:Peptides; D011506:Proteins; D029701:Saccharomyces cerevisiae Proteins