title: Tandem Mass Spectrum Identification via Cascaded Search.
abstract: Accurate assignment of peptide sequences to observed fragmentation spectra is hindered by the large number of hypotheses that must be considered for each observed spectrum. A high score assigned to a particular peptide-spectrum match (PSM) may not end up being statistically significant after multiple testing correction. Researchers can mitigate this problem by controlling the hypothesis space in various ways: considering only peptides resulting from enzymatic cleavages, ignoring possible post-translational modifications or single nucleotide variants, etc. However, these strategies sacrifice identifications of spectra generated by rarer types of peptides. In this work, we introduce a statistical testing framework, cascade search, that directly addresses this problem. The method requires that the user specify a priori a statistical confidence threshold as well as a series of peptide databases. For instance, such a cascade of databases could include fully tryptic, semitryptic, and nonenzymatic peptides or peptides with increasing numbers of modifications. Cascaded search then gradually expands the list of candidate peptides from more likely peptides toward rare peptides, sequestering at each stage any spectrum that is identified with a specified statistical confidence. We compare cascade search to a standard procedure that lumps all of the peptides into a single database, as well as to a previously described group FDR procedure that computes the FDR separately within each database. We demonstrate, using simulated and real data, that cascade search identifies more spectra at a fixed FDR threshold than with either the ungrouped or grouped approach. Cascade search thus provides a general method for maximizing the number of identified spectra in a statistically rigorous fashion.
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States.|School of Mathematics and Statistics, University of Sydney, Camperdown, NSW 2006, Australia.|Department of Genome Sciences, Department of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States.
authors.forename: Attila
authors.initials: A
authors.lastname: Kertesz-Farkas
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States.|School of Mathematics and Statistics, University of Sydney, Camperdown, NSW 2006, Australia.|Department of Genome Sciences, Department of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States.
authors.forename: Uri
authors.initials: U
authors.lastname: Keich
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States.|School of Mathematics and Statistics, University of Sydney, Camperdown, NSW 2006, Australia.|Department of Genome Sciences, Department of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States.
authors.forename: William Stafford
authors.initials: WS
authors.lastname: Noble
mesh_terms: D000465:Algorithms; D002460:Cell Line; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D010455:Peptides / Q000032:analysis* / Q000378:metabolism; D020033:Protein Isoforms / Q000032:analysis / Q000378:metabolism; D011499:Protein Processing, Post-Translational; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D029701:Saccharomyces cerevisiae Proteins / Q000032:analysis / Q000378:metabolism; D053719:Tandem Mass Spectrometry / Q000379:methods*
chemical_list: D010455:Peptides; D020033:Protein Isoforms; D029701:Saccharomyces cerevisiae Proteins
keywords: FDR control; Peptide assignment; spectrum identification