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Sun, B.* ; Smialowski, P. ; Aftab, W.* ; Schmidt, A.* ; Forne, I.* ; Straub, T.* ; Imhof, A.*

Improving SWATH-MS analysis by deep-learning.

Proteomics 23:e2200179 (2023)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
Data-independent acquisition (DIA) of tandem mass spectrometry spectra has emerged as a promising technology to improve coverage and quantification of proteins in complex mixtures. The success of DIA experiments is dependent on the quality of spectral libraries used for data base searching. Frequently, these libraries need to be generated by labor and time intensive data dependent acquisition (DDA) experiments. Recently, several algorithms have been published that allow the generation of theoretical libraries by an efficient prediction of retention time and intensity of the fragment ions. Sequential windowed acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) is a DIA method that can be applied at an unprecedented speed, but the fragmentation spectra suffer from a lower quality than data acquired on Orbitrap instruments. To reliably generate theoretical libraries that can be used in SWATH experiments, we developed deep-learning for SWATH analysis (dpSWATH), to improve the sensitivity and specificity of data generated by Q-TOF mass spectrometers. The theoretical library built by dpSWATH allowed us to increase the identification rate of proteins compared to traditional or library-free methods. Based on our analysis we conclude that dpSWATH is a superior prediction framework for SWATH-MS measurements than other algorithms based on Orbitrap data.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Data Independent Acquisition ; Deep Learning ; Proteomics ; Spectral Library; Acquisition; Prediction; Peptides; Spectra
ISSN (print) / ISBN 1615-9853
e-ISSN 1615-9861
Journal Proteomics
Quellenangaben Volume: 23, Issue: 9, Pages: , Article Number: e2200179 Supplement: ,
Publisher Wiley
Publishing Place 111 River St, Hoboken 07030-5774, Nj Usa
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Stem Cell Research (ISF)
Grants China Scholarship Council
Bundesministerium für Bildung und Forschung
Deutsche Forschungsgemeinschaft