Spectral Probabilities and Generating Functions of Tandem Mass Spectra: A Strike against Decoy Databases

@article{citeulike:2959654, abstract = {Abstract: 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 ”-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.}, address = {Department of Computer Science and Engineering, University of California San Diego, La Jolla California 92093, and Bioinformatics Program, University of California San Diego, La Jolla California 92093}, author = {Kim, Sangtae and Gupta, Nitin and Pevzner, Pavel A. }, citeulike-article-id = {2959654}, doi = {http://dx.doi.org/10.1021/pr8001244}, journal = {J. Proteome Res.}, keywords = {bioinformatics, fdr, proteomics}, month = {July}, posted-at = {2008-07-03 16:08:04}, priority = {2}, title = {Spectral Probabilities and Generating Functions of Tandem Mass Spectra: A Strike against Decoy Databases}, url = {http://dx.doi.org/10.1021/pr8001244}, year = {2008} }

TY - JOUR ID - citeulike:2959654 L3 - citeulike-article-id:2959654 TI - Spectral Probabilities and Generating Functions of Tandem Mass Spectra: A Strike against Decoy Databases JF - J. Proteome Res. AD - Department of Computer Science and Engineering, University of California San Diego, La Jolla California 92093, and Bioinformatics Program, University of California San Diego, La Jolla California 92093 N2 - Abstract: 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 ”-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. KW - bioinformatics KW - fdr KW - proteomics AU - Kim, Sangtae AU - Gupta, Nitin AU - Pevzner, Pavel PY - 2008/07/03/ UR - http://dx.doi.org/10.1021/pr8001244 ER -