CFM-ID 4.0: More Accurate ESI-MS/MS Spectral Prediction and Compound Identification.
Journal
Analytical chemistry
ISSN: 1520-6882
Titre abrégé: Anal Chem
Pays: United States
ID NLM: 0370536
Informations de publication
Date de publication:
31 08 2021
31 08 2021
Historique:
pubmed:
18
8
2021
medline:
11
9
2021
entrez:
17
8
2021
Statut:
ppublish
Résumé
In the field of metabolomics, mass spectrometry (MS) is the method most commonly used for identifying and annotating metabolites. As this typically involves matching a given MS spectrum against an experimentally acquired reference spectral library, this approach is limited by the coverage and size of such libraries (which typically number in the thousands). These experimental libraries can be greatly extended by predicting the MS spectra of known chemical structures (which number in the millions) to create computational reference spectral libraries. To facilitate the generation of predicted spectral reference libraries, we developed CFM-ID, a computer program that can accurately predict ESI-MS/MS spectrum for a given compound structure. CFM-ID is one of the best-performing methods for compound-to-mass-spectrum prediction and also one of the top tools for
Identifiants
pubmed: 34403256
doi: 10.1021/acs.analchem.1c01465
pmc: PMC9064193
mid: NIHMS1786930
doi:
Substances chimiques
Flavones
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
11692-11700Subventions
Organisme : NIEHS NIH HHS
ID : U2C ES030170
Pays : United States
Organisme : CIHR
Pays : Canada
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