Spectral Probabilities and Generating Functions of Tandem Mass Spectra: a Strike Against Decoy Databases

By Sangtae Kim, Nitin Gupta, Pavel A. Pevzner. Journal of Proteome Research, 2008

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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 Δ-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 trade-off 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.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/3f4c9eb1e1600543b3cbf79a1c71931909627aed

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Citations in the corpus

A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics (2010, background): “gov/staff/slottad/MassSieve/ b MS-GF [168] proteomics.”
Universal database search tool for proteomics (2014, neutral): “At the same time, it makes scoring and computing accurate E -values fast 15 (second and third condition).”
Fast and Accurate Protein False Discovery Rates on Large-Scale Proteomics Data Sets with Percolator 3.0 (2016, neutral): “Tandem [4], and MS-GF+ [5].”
iProphet: Multi-level Integrative Analysis of Shotgun Proteomic Data Improves Peptide and Protein Identification Rates and Error Estimates* (2011, background): “An alternative approach, which falls in the same category of single-spectrum statistical confidence scores as E -values, is based on the concept of generating functions (47).”
mProphet: automated data processing and statistical validation for large-scale SRM experiments (2011, methodology): “mProphet estimates a FDR, which can be used to filter the data according to a user-defined quality; this is analogous to the filtering of tandem mass spectrometry (MS) identificatio”