title: A hierarchical statistical model to assess the confidence of peptides and proteins inferred from tandem mass spectrometry.
abstract: BACKGROUND
  Statistical evaluation of the confidence of peptide and protein identifications made by tandem mass spectrometry is a critical component for appropriately interpreting the experimental data and conducting downstream analysis. Although many approaches have been developed to assign confidence measure from different perspectives, a unified statistical framework that integrates the uncertainty of peptides and proteins is still missing.
  
  
  RESULTS
  We developed a hierarchical statistical model (HSM) that jointly models the uncertainty of the identified peptides and proteins and can be applied to any scoring system. With data sets of a standard mixture and the yeast proteome, we demonstrate that the HSM offers a reliable or at least conservative false discovery rate (FDR) estimate for peptide and protein identifications. The probability measure of HSM also offers a powerful discriminating score for peptide identification.
  
  
  BACKGROUND
  The algorithm is available upon request from the authors.
authors.affiliation: Division of Biostatistics, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA. chashen@iupui.edu
authors.forename: Changyu
authors.initials: C
authors.lastname: Shen
authors.forename: Zhiping
authors.initials: Z
authors.lastname: Wang
authors.forename: Ganesh
authors.initials: G
authors.lastname: Shankar
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhang
authors.forename: Lang
authors.initials: L
authors.lastname: Li
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D013058:Mass Spectrometry / Q000379:methods*; D008956:Models, Chemical; D015233:Models, Statistical; D010449:Peptide Mapping / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D020539:Sequence Analysis, Protein / Q000379:methods*