Generating high quality libraries for DIA MS with empirically corrected peptide predictions.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
25 03 2020
25 03 2020
Historique:
received:
21
08
2019
accepted:
28
02
2020
entrez:
28
3
2020
pubmed:
28
3
2020
medline:
15
7
2020
Statut:
epublish
Résumé
Data-independent acquisition approaches typically rely on experiment-specific spectrum libraries, requiring offline fractionation and tens to hundreds of injections. We demonstrate a library generation workflow that leverages fragmentation and retention time prediction to build libraries containing every peptide in a proteome, and then refines those libraries with empirical data. Our method specifically enables rapid, experiment-specific library generation for non-model organisms, which we demonstrate using the malaria parasite Plasmodium falciparum, and non-canonical databases, which we show by detecting missense variants in HeLa.
Identifiants
pubmed: 32214105
doi: 10.1038/s41467-020-15346-1
pii: 10.1038/s41467-020-15346-1
pmc: PMC7096433
doi:
Substances chimiques
Peptide Library
0
Peptides
0
Proteome
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1548Subventions
Organisme : NIAID NIH HHS
ID : K25 AI119229
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM133981
Pays : United States
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