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Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF
View ORCID ProfileCharlotte Adams, View ORCID ProfileWassim Gabriel, View ORCID ProfileKris Laukens, View ORCID ProfileMario Picciani, View ORCID ProfileMathias Wilhelm, View ORCID ProfileWout Bittremieux, View ORCID ProfileKurt Boonen
doi: https://doi.org/10.1101/2023.07.17.549401
Charlotte Adams
1Department of Computer Science, University of Antwerp, Antwerp, Belgium
Wassim Gabriel
2Computational Mass Spectrometry, Technical University of Munich, 85354 Freising, Germany
Kris Laukens
1Department of Computer Science, University of Antwerp, Antwerp, Belgium
Mario Picciani
2Computational Mass Spectrometry, Technical University of Munich, 85354 Freising, Germany
Mathias Wilhelm
2Computational Mass Spectrometry, Technical University of Munich, 85354 Freising, Germany
3Munich Data Science Institute, Technical University of Munich, 85748 Garching, Germany
Wout Bittremieux
1Department of Computer Science, University of Antwerp, Antwerp, Belgium
Kurt Boonen
4Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
5Sustainable Health Department, Flemish Institute for Technological Research (VITO), Antwerp, Belgium

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Posted February 01, 2024.
Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF
Charlotte Adams, Wassim Gabriel, Kris Laukens, Mario Picciani, Mathias Wilhelm, Wout Bittremieux, Kurt Boonen
bioRxiv 2023.07.17.549401; doi: https://doi.org/10.1101/2023.07.17.549401
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