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
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
535-546Références
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