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Improving SWATH-MS analysis by deep-learning.
Sun, Bo; Smialowski, Pawel; Aftab, Wasim; Schmidt, Andreas; Forne, Ignasi; Straub, Tobias; Imhof, Axel.
Afiliação
  • Sun B; Faculty of Medicine, Biomedical Center, Protein Analysis Unit, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany.
  • Smialowski P; Institute of Stem Cell Research, Helmholtz Center Munich, German Research Center for Environmental Health, Germany.
  • Aftab W; Faculty of Medicine, Biomedical Center, Computational Biology Unit, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany.
  • Schmidt A; Faculty of Medicine, Biomedical Center, Protein Analysis Unit, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany.
  • Forne I; Faculty of Medicine, Biomedical Center, Protein Analysis Unit, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany.
  • Straub T; Faculty of Medicine, Biomedical Center, Protein Analysis Unit, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany.
  • Imhof A; Faculty of Medicine, Biomedical Center, Computational Biology Unit, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany.
Proteomics ; 23(9): e2200179, 2023 05.
Article em En | MEDLINE | ID: mdl-36571325
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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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Idioma: En Ano de publicação: 2023 Tipo de documento: Article