title: Probabilistic consensus scoring improves tandem mass spectrometry peptide identification.
abstract: Database search is a standard technique for identifying peptides from their tandem mass spectra. To increase the number of correctly identified peptides, we suggest a probabilistic framework that allows the combination of scores from different search engines into a joint consensus score. Central to the approach is a novel method to estimate scores for peptides not found by an individual search engine. This approach allows the estimation of p-values for each candidate peptide and their combination across all search engines. The consensus approach works better than any single search engine across all different instrument types considered in this study. Improvements vary strongly from platform to platform and from search engine to search engine. Compared to the industry standard MASCOT, our approach can identify up to 60% more peptides. The software for consensus predictions is implemented in C++ as part of OpenMS, a software framework for mass spectrometry. The source code is available in the current development version of OpenMS and can easily be used as a command line application or via a graphical pipeline designer TOPPAS.
authors.affiliation: Center for Bioinformatics, Eberhard Karls University, 72076 Tübingen, Germany. sven.nahnsen@uni-tuebingen.de
authors.forename: Sven
authors.initials: S
authors.lastname: Nahnsen
authors.forename: Andreas
authors.initials: A
authors.lastname: Bertsch
authors.forename: Jörg
authors.initials: J
authors.lastname: Rahnenführer
authors.forename: Alfred
authors.initials: A
authors.lastname: Nordheim
authors.forename: Oliver
authors.initials: O
authors.lastname: Kohlbacher
mesh_terms: D030562:Databases, Protein; D016247:Information Storage and Retrieval; D008962:Models, Theoretical; D010455:Peptides / Q000737:chemistry*; D011336:Probability; D053719:Tandem Mass Spectrometry / Q000379:methods*
chemical_list: D010455:Peptides