Removing the Hidden Data Dependency of DIA with Predicted Spectral Libraries.
bioinformatics
data-independent acquisition
label-free quantification
peptide-centric
Journal
Proteomics
ISSN: 1615-9861
Titre abrégé: Proteomics
Pays: Germany
ID NLM: 101092707
Informations de publication
Date de publication:
02 2020
02 2020
Historique:
received:
19
09
2019
revised:
20
12
2019
pubmed:
26
1
2020
medline:
22
12
2020
entrez:
26
1
2020
Statut:
ppublish
Résumé
Data-independent acquisition (DIA) generates comprehensive yet complex mass spectrometric data, which imposes the use of data-dependent acquisition (DDA) libraries for deep peptide-centric detection. Here, it is shown that DIA can be redeemed from this dependency by combining predicted fragment intensities and retention times with narrow window DIA. This eliminates variation in library building and omits stochastic sampling, finally making the DIA workflow fully deterministic. Especially for clinical proteomics, this has the potential to facilitate inter-laboratory comparison.
Identifiants
pubmed: 31981311
doi: 10.1002/pmic.201900306
doi:
Substances chimiques
Peptide Library
0
Peptides
0
Proteome
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1900306Informations de copyright
© 2020 The Authors. Proteomics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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