A hierarchical statistical model to assess the confidence of peptides and proteins inferred from tandem mass spectrometry.
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.
Shen
Changyu
C
Division of Biostatistics, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA. chashen@iupui.edu
Wang
Zhiping
Z
Shankar
Ganesh
G
Zhang
Xiang
X
Li
Lang
L
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*