High-performance peptide identification by tandem mass spectrometry allows reliable automatic data processing in proteomics.
In a previous paper we introduced a novel model-based approach (OLAV) to the problem of identifying peptides via tandem mass spectrometry, for which early implementations showed promising performance. We recently further improved this performance to a remarkable level (1-2% false positive rate at 95% true positive rate) and characterized key properties of OLAV like robustness and training set size. We present these results in a synthetic and coherent way along with detailed performance comparisons, a new scoring component making use of peptide amino acidic composition, and new developments like automatic parameter learning. Finally, we discuss the impact of OLAV on the automation of proteomics projects.
Colinge
Jacques
J
GeneProt Inc., Rue Pré de la Fontaine 2, Case Postale 125, 1217 Meyrin, Switzerland. jacques.collinge@geneprot.com
Masselot
Alexandre
A
Cusin
Isabelle
I
Mahé
Eve
E
Niknejad
Anne
A
Argoud-Puy
Ghislaine
G
Reffas
Samia
S
Bederr
Nassima
N
Gleizes
Anne
A
Rey
Pierre-Antoine
PA
Bougueleret
Lydie
L
D000465:Algorithms; D001331:Automation; D005189:False Positive Reactions; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods; D010455:Peptides / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D012372:ROC Curve; D021241:Spectrometry, Mass, Electrospray Ionization / Q000379:methods; D013997:Time Factors
D010455:Peptides