Liu, XiaowenSegar, Matthew W.Li, Shuai ChengKim, Sangtae2025-04-162025-04-162014Liu X, Segar MW, Li SC, Kim S. Spectral probabilities of top-down tandem mass spectra. BMC Genomics. 2014;15 Suppl 1(Suppl 1):S9. doi:10.1186/1471-2164-15-S1-S9https://hdl.handle.net/1805/47061Background: In mass spectrometry-based proteomics, the statistical significance of a peptide-spectrum or protein-spectrum match is an important indicator of the correctness of the peptide or protein identification. In bottom-up mass spectrometry, probabilistic models, such as the generating function method, have been successfully applied to compute the statistical significance of peptide-spectrum matches for short peptides containing no post-translational modifications. As top-down mass spectrometry, which often identifies intact proteins with post-translational modifications, becomes available in many laboratories, the estimation of statistical significance of top-down protein identification results has come into great demand. Results: In this paper, we study an extended generating function method for accurately computing the statistical significance of protein-spectrum matches with post-translational modifications. Experiments show that the extended generating function method achieves high accuracy in computing spectral probabilities and false discovery rates. Conclusions: The extended generating function method is a non-trivial extension of the generating function method for bottom-up mass spectrometry. It can be used to choose the correct protein-spectrum match from several candidate protein-spectrum matches for a spectrum, as well as separate correct protein-spectrum matches from incorrect ones identified from a large number of tandem mass spectra.en-USAttribution 4.0 InternationalComputational biologyStatistical modelsProteinsProteomicsArticle