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Titel:

Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Gessulat, Siegfried; Schmidt, Tobias; Zolg, Daniel Paul; Samaras, Patroklos; Schnatbaum, Karsten; Zerweck, Johannes; Knaute, Tobias; Rechenberger, Julia; Delanghe, Bernard; Huhmer, Andreas; Reimer, Ulf; Ehrlich, Hans-Christian; Aiche, Stephan; Kuster, Bernhard; Wilhelm, Mathias
Abstract:
In mass-spectrometry-based proteomics, the identification and quantification of peptides and proteins heavily rely on sequence database searching or spectral library matching. The lack of accurate predictive models for fragment ion intensities impairs the realization of the full potential of these approaches. Here, we extended the ProteomeTools synthetic peptide library to 550,000 tryptic peptides and 21 million high-quality tandem mass spectra. We trained a deep neural network, termed Prosit, r...     »
Stichworte:
BayBioMS
Zeitschriftentitel:
Nature Methods
Jahr:
2019
Band / Volume:
16
Heft / Issue:
6
Seitenangaben Beitrag:
509-518
Volltext / DOI:
doi:10.1038/s41592-019-0426-7
Verlag / Institution:
Springer Science and Business Media LLC
E-ISSN:
1548-70911548-7105
Publikationsdatum:
27.05.2019
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