title: Determining the calibration of confidence estimation procedures for unique peptides in shotgun proteomics.
abstract: The analysis of a shotgun proteomics experiment results in a list of peptide-spectrum matches (PSMs) in which each fragmentation spectrum has been matched to a peptide in a database. Subsequently, most protein inference algorithms rank peptides according to the best-scoring PSM for each peptide. However, there is disagreement in the scientific literature on the best method to assess the statistical significance of the resulting peptide identifications. Here, we use a previously described calibration protocol to evaluate the accuracy of three different peptide-level statistical confidence estimation procedures: the classical Fisher's method, and two complementary procedures that estimate significance, respectively, before and after selecting the top-scoring PSM for each spectrum. Our experiments show that the latter method, which is employed by MaxQuant and Percolator, produces the most accurate, well-calibrated results.
authors.affiliation: Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.
authors.forename: Viktor
authors.initials: V
authors.lastname: Granholm
authors.affiliation: Science for Life Laboratory, School of Biotechnology, Royal Institute of Technology (KTH), Solna, Sweden.
authors.forename: José Fernández
authors.initials: JF
authors.lastname: Navarro
authors.affiliation: Department of Genome Sciences, Department of Computer Science and Engineering, University of Washington, USA.
authors.forename: William Stafford
authors.initials: WS
authors.lastname: Noble
authors.affiliation: Science for Life Laboratory, School of Biotechnology, Royal Institute of Technology (KTH), Solna, Sweden. Electronic address: lukas.kall@scilifelab.se.
authors.forename: Lukas
authors.initials: L
authors.lastname: Käll
mesh_terms: D000465:Algorithms; D002138:Calibration; D019295:Computational Biology; D005189:False Positive Reactions; D015233:Models, Statistical; D010455:Peptides / Q000737:chemistry*; D011336:Probability; D020543:Proteome; D040901:Proteomics / Q000379:methods*
chemical_list: D010455:Peptides; D020543:Proteome