title: OLAV: towards high-throughput tandem mass spectrometry data identification.
abstract: Mass spectrometry combined with database searching has become the preferred method for identifying proteins in proteomics projects. Proteins are digested by one or several enzymes to obtain peptides, which are analyzed by mass spectrometry. We introduce a new family of scoring schemes, named OLAV, aimed at identifying peptides in a database from their tandem mass spectra. OLAV scoring schemes are based on signal detection theory, and exploit mass spectrometry information more extensively than previously existing schemes. We also introduce a new concept of structural matching that uses pattern detection methods to better separate true from false positives. We show the superiority of OLAV scoring schemes compared to MASCOT, a widely used identification program. We believe that this work introduces a new way of designing scoring schemes that are especially adapted to high-throughput projects such as GeneProt large-scale human plasma project, where it is impractical to check all identifications manually.
authors.affiliation: GeneProt Inc., Pré de la Fontaine 2, CH-1217 Meyrin, Switzerland. jacques.colinge@geneprot.com
authors.forename: Jacques
authors.initials: J
authors.lastname: Colinge
authors.forename: Alexandre
authors.initials: A
authors.lastname: Masselot
authors.forename: Marc
authors.initials: M
authors.lastname: Giron
authors.forename: Thierry
authors.initials: T
authors.lastname: Dessingy
authors.forename: Jérôme
authors.initials: J
authors.lastname: Magnin
mesh_terms: D001499:Bayes Theorem; D013058:Mass Spectrometry / Q000379:methods*; D040901:Proteomics