MSLibrarian: Optimized Predicted Spectral Libraries for Data-Independent Acquisition Proteomics.

R-software data-independent acquisition deep-learning proteomics spectral predictions

Journal

Journal of proteome research
ISSN: 1535-3907
Titre abrégé: J Proteome Res
Pays: United States
ID NLM: 101128775

Informations de publication

Date de publication:
04 02 2022
Historique:
pubmed: 20 1 2022
medline: 1 3 2022
entrez: 19 1 2022
Statut: ppublish

Résumé

Data-independent acquisition-mass spectrometry (DIA-MS) is the method of choice for deep, consistent, and accurate single-shot profiling in bottom-up proteomics. While classic workflows for targeted quantification from DIA-MS data require auxiliary data-dependent acquisition (DDA) MS analysis of subject samples to derive prior-knowledge spectral libraries, library-free approaches based on

Identifiants

pubmed: 35042333
doi: 10.1021/acs.jproteome.1c00796
pmc: PMC8822486
doi:

Substances chimiques

Peptides 0
Proteins 0
Proteome 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

535-546

Références

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Auteurs

Marc Isaksson (M)

Department of Biomedical Engineering, Lund University, 22100 Lund, Sweden.
Department of Experimental Medical Science and Wallenberg Center for Molecular Medicine, Lund University, 22100 Lund, Sweden.

Christofer Karlsson (C)

Infection Medicine Proteomics Lab, Division of Infection Medicine (BMC), Faculty of Medicine, Lund University, 22100 Lund, Sweden.

Thomas Laurell (T)

Department of Biomedical Engineering, Lund University, 22100 Lund, Sweden.

Agnete Kirkeby (A)

Department of Experimental Medical Science and Wallenberg Center for Molecular Medicine, Lund University, 22100 Lund, Sweden.
Department of Neuroscience, University of Copenhagen, DK-2200 Copenhagen, Denmark.
The Novo Nordisk Foundation Center for Stem Cell Biology (DanStem), Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark.

Moritz Heusel (M)

Infection Medicine Proteomics Lab, Division of Infection Medicine (BMC), Faculty of Medicine, Lund University, 22100 Lund, Sweden.

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Classifications MeSH