Journal
JOURNAL OF PROTEOME RESEARCH
Volume 7, Issue 8, Pages 3354-3363Publisher
AMER CHEMICAL SOC
DOI: 10.1021/pr8001244
Keywords
spectral probability; peptide identification; decoy database; false discovery rate
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Funding
- Howard Hughes Medical Institute Funding Source: Medline
- NCRR NIH HHS [R01 RR016522-01A1, 1 R01 RR 16522, R01 RR016522] Funding Source: Medline
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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 A-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.
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