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Ionbot : a novel, innovative and sensitive machine learning approach to LC-MS/MS peptide identification

Sven Degroeve (UGent) , Ralf Gabriels (UGent) , Kevin Velghe (UGent) , Robbin Bouwmeester (UGent) , Natalia Tichshenko (UGent) and Lennart Martens (UGent)
(2022) BIORXIV.
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Abstract
Mass spectrometry-based proteomics generates vast amounts of signal data that require computational interpretation to obtain peptide identifications. Dozens of algorithms for this task exist, but all exploit only part of the acquired data to judge a peptide-to-spectrum match (PSM), ignoring important information such as the observed retention time and fragment ion peak intensity pattern. Moreover, only few identification algorithms allow open modification searches that can substantially increase peptide identifications. We here therefore introduce ionbot, a novel open modification search engine that is the first to fully merge machine learning with peptide identification. This core innovation brings the ability to include a much larger range of experimental data into PSM scoring, and even to adapt this scoring to the specifics of the data itself. As a result, ionbot substantially increases PSM confidence for open searches, and even enables a further increase in peptide identification rate of up to 30% by also considering highly plausible, lower-ranked, co-eluting matches for a fragmentation spectrum. Moreover, the exclusive use of machine learning for scoring also means that any future improvements to predictive models for peptide behavior will also result in more sensitive and accurate peptide identification.

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MLA
Degroeve, Sven, et al. “Ionbot : A Novel, Innovative and Sensitive Machine Learning Approach to LC-MS/MS Peptide Identification.” BIORXIV, 2022, doi:10.1101/2021.07.02.450686.
APA
Degroeve, S., Gabriels, R., Velghe, K., Bouwmeester, R., Tichshenko, N., & Martens, L. (2022). Ionbot : a novel, innovative and sensitive machine learning approach to LC-MS/MS peptide identification. https://doi.org/10.1101/2021.07.02.450686
Chicago author-date
Degroeve, Sven, Ralf Gabriels, Kevin Velghe, Robbin Bouwmeester, Natalia Tichshenko, and Lennart Martens. 2022. “Ionbot : A Novel, Innovative and Sensitive Machine Learning Approach to LC-MS/MS Peptide Identification.” BIORXIV. https://doi.org/10.1101/2021.07.02.450686.
Chicago author-date (all authors)
Degroeve, Sven, Ralf Gabriels, Kevin Velghe, Robbin Bouwmeester, Natalia Tichshenko, and Lennart Martens. 2022. “Ionbot : A Novel, Innovative and Sensitive Machine Learning Approach to LC-MS/MS Peptide Identification.” BIORXIV. doi:10.1101/2021.07.02.450686.
Vancouver
1.
Degroeve S, Gabriels R, Velghe K, Bouwmeester R, Tichshenko N, Martens L. Ionbot : a novel, innovative and sensitive machine learning approach to LC-MS/MS peptide identification. BIORXIV. 2022.
IEEE
[1]
S. Degroeve, R. Gabriels, K. Velghe, R. Bouwmeester, N. Tichshenko, and L. Martens, “Ionbot : a novel, innovative and sensitive machine learning approach to LC-MS/MS peptide identification,” BIORXIV. 2022.
@misc{8737651,
  abstract     = {{Mass spectrometry-based proteomics generates vast amounts of signal data that require computational interpretation to obtain peptide identifications. Dozens of algorithms for this task exist, but all exploit only part of the acquired data to judge a peptide-to-spectrum match (PSM), ignoring important information such as the observed retention time and fragment ion peak intensity pattern. Moreover, only few identification algorithms allow open modification searches that can substantially increase peptide identifications. We here therefore introduce ionbot, a novel open modification search engine that is the first to fully merge machine learning with peptide identification. This core innovation brings the ability to include a much larger range of experimental data into PSM scoring, and even to adapt this scoring to the specifics of the data itself. As a result, ionbot substantially increases PSM confidence for open searches, and even enables a further increase in peptide identification rate of up to 30% by also considering highly plausible, lower-ranked, co-eluting matches for a fragmentation spectrum. Moreover, the exclusive use of machine learning for scoring also means that any future improvements to predictive models for peptide behavior will also result in more sensitive and accurate peptide identification.}},
  author       = {{Degroeve, Sven and Gabriels, Ralf and Velghe, Kevin and Bouwmeester, Robbin and Tichshenko, Natalia and Martens, Lennart}},
  language     = {{eng}},
  series       = {{BIORXIV}},
  title        = {{Ionbot : a novel, innovative and sensitive machine learning approach to LC-MS/MS peptide identification}},
  url          = {{http://doi.org/10.1101/2021.07.02.450686}},
  year         = {{2022}},
}
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