Spectral probabilities and generating functions of tandem mass spectra: a strike against decoy databases.
A key problem in computational proteomics is distinguishing between correct and false peptide identifications. We argue that evaluating the error rates of peptide identifications is not unlike computing generating functions in combinatorics. We show that the generating functions and their derivatives ( spectral energy and spectral probability) represent new features of tandem mass spectra that, similarly to Delta-scores, significantly improve peptide identifications. Furthermore, the spectral probability provides a rigorous solution to the problem of computing statistical significance of spectral identifications. The spectral energy/probability approach improves the sensitivity-specificity tradeoff of existing MS/MS search tools, addresses the notoriously difficult problem of "one-hit-wonders" in mass spectrometry, and often eliminates the need for decoy database searches. We therefore argue that the generating function approach has the potential to increase the number of peptide identifications in MS/MS searches.
Kim
Sangtae
S
Department of Computer Science and Engineering, University of California San Diego, La Jolla CA 92093, USA.
Gupta
Nitin
N
Pevzner
Pavel A
PA
D001426:Bacterial Proteins / Q000032:analysis; D003627:Data Interpretation, Statistical; D016208:Databases, Factual / Q000706:statistics & numerical data*; D010455:Peptides / Q000032:analysis*; D011336:Probability; D040901:Proteomics / Q000706:statistics & numerical data*; D020592:Shewanella / Q000737:chemistry; D053719:Tandem Mass Spectrometry / Q000706:statistics & numerical data*
D001426:Bacterial Proteins; D010455:Peptides