title: False discovery rates and related statistical concepts in mass spectrometry-based proteomics.
abstract: Development of statistical methods for assessing the significance of peptide assignments to tandem mass spectra obtained using database searching remains an important problem. In the past several years, several different approaches have emerged, including the concept of expectation values, target-decoy strategy, and the probability mixture modeling approach of PeptideProphet. In this work, we provide a background on statistical significance analysis in the field of mass spectrometry-based proteomics, and present our perspective on the current and future developments in this area.
authors: Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA.
authors: Hyungwon
authors: H
authors: Choi
authors: Alexey I
authors: AI
authors: Nesvizhskii
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D015233:Models, Statistical; D010455:Peptides / Q000032:analysis*; D040901:Proteomics / Q000379:methods; D053719:Tandem Mass Spectrometry / Q000379:methods*
chemical_list: D010455:Peptides