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Spectral prediction features as a solution for the search space size problem in proteogenomics

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Abstract
Proteogenomics approaches often struggle with the distinction between true and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for peptide-to-spectrum matching in a proteogenomics context. To that end, features from the spectral intensity pattern predictors MS2PIP and Prosit were combined with the canonical scores from MaxQuant in the Percolator postprocessing tool for protein sequence databases constructed out of ribosome profiling and nanopore RNA-Seq analyses. The presented results provide evidence that this approach enhances both the identification rate as well as the validation stringency in a proteogenomic setting.
Keywords
Analytical Chemistry, Biochemistry, Molecular Biology, FALSE DISCOVERY RATES, PEPTIDE IDENTIFICATION, PROTEIN IDENTIFICATION, RNA-SEQ, IN-VIVO, TRANSLATION, PROTEOMICS, TANDEM, DATABASES, ENABLES

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MLA
Verbruggen, Steven, et al. “Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics.” MOLECULAR & CELLULAR PROTEOMICS, vol. 20, 2021, doi:10.1016/j.mcpro.2021.100076.
APA
Verbruggen, S., Gessulat, S., Gabriels, R., Matsaroki, A., Van de Voorde, H., Kuster, B., … Menschaert, G. (2021). Spectral prediction features as a solution for the search space size problem in proteogenomics. MOLECULAR & CELLULAR PROTEOMICS, 20. https://doi.org/10.1016/j.mcpro.2021.100076
Chicago author-date
Verbruggen, Steven, Siegfried Gessulat, Ralf Gabriels, Anna Matsaroki, Hendrik Van de Voorde, Bernhard Kuster, Sven Degroeve, et al. 2021. “Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics.” MOLECULAR & CELLULAR PROTEOMICS 20. https://doi.org/10.1016/j.mcpro.2021.100076.
Chicago author-date (all authors)
Verbruggen, Steven, Siegfried Gessulat, Ralf Gabriels, Anna Matsaroki, Hendrik Van de Voorde, Bernhard Kuster, Sven Degroeve, Lennart Martens, Wim Van Criekinge, Mathias Wilhelm, and Gerben Menschaert. 2021. “Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics.” MOLECULAR & CELLULAR PROTEOMICS 20. doi:10.1016/j.mcpro.2021.100076.
Vancouver
1.
Verbruggen S, Gessulat S, Gabriels R, Matsaroki A, Van de Voorde H, Kuster B, et al. Spectral prediction features as a solution for the search space size problem in proteogenomics. MOLECULAR & CELLULAR PROTEOMICS. 2021;20.
IEEE
[1]
S. Verbruggen et al., “Spectral prediction features as a solution for the search space size problem in proteogenomics,” MOLECULAR & CELLULAR PROTEOMICS, vol. 20, 2021.
@article{8716353,
  abstract     = {{Proteogenomics approaches often struggle with the distinction between true and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for peptide-to-spectrum matching in a proteogenomics context. To that end, features from the spectral intensity pattern predictors MS2PIP and Prosit were combined with the canonical scores from MaxQuant in the Percolator postprocessing tool for protein sequence databases constructed out of ribosome profiling and nanopore RNA-Seq analyses. The presented results provide evidence that this approach enhances both the identification rate as well as the validation stringency in a proteogenomic setting.}},
  articleno    = {{100076}},
  author       = {{Verbruggen, Steven and Gessulat, Siegfried and Gabriels, Ralf and Matsaroki, Anna and Van de Voorde, Hendrik and Kuster, Bernhard and Degroeve, Sven and Martens, Lennart and Van Criekinge, Wim and Wilhelm, Mathias and Menschaert, Gerben}},
  issn         = {{1535-9476}},
  journal      = {{MOLECULAR & CELLULAR PROTEOMICS}},
  keywords     = {{Analytical Chemistry,Biochemistry,Molecular Biology,FALSE DISCOVERY RATES,PEPTIDE IDENTIFICATION,PROTEIN IDENTIFICATION,RNA-SEQ,IN-VIVO,TRANSLATION,PROTEOMICS,TANDEM,DATABASES,ENABLES}},
  language     = {{eng}},
  pages        = {{13}},
  title        = {{Spectral prediction features as a solution for the search space size problem in proteogenomics}},
  url          = {{http://dx.doi.org/10.1016/j.mcpro.2021.100076}},
  volume       = {{20}},
  year         = {{2021}},
}

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