Lipid Annotator: Towards Accurate Annotation in Non-Targeted Liquid Chromatography High-Resolution Tandem Mass Spectrometry (LC-HRMS/MS) Lipidomics Using A Rapid and User-Friendly Software.
automation
ion mobility
lipid annotation
lipidomics
liquid chromatography
metabolomics
software
tandem mass spectrometry
time-of-flight
Journal
Metabolites
ISSN: 2218-1989
Titre abrégé: Metabolites
Pays: Switzerland
ID NLM: 101578790
Informations de publication
Date de publication:
12 Mar 2020
12 Mar 2020
Historique:
received:
28
01
2020
revised:
28
02
2020
accepted:
06
03
2020
entrez:
18
3
2020
pubmed:
18
3
2020
medline:
18
3
2020
Statut:
epublish
Résumé
Lipidomics has great promise in various applications; however, a major bottleneck in lipidomics is the accurate and comprehensive annotation of high-resolution tandem mass spectral data. While the number of available lipidomics software has drastically increased over the past five years, the reduction of false positives and the realization of obtaining structurally accurate annotations remains a significant challenge. We introduce Lipid Annotator, which is a user-friendly software for lipidomic analysis of data collected by liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). We validate annotation accuracy against lipid standards and other lipidomics software. Lipid Annotator was integrated into a workflow applying an iterative exclusion MS/MS acquisition strategy to National Institute of Standards and Technology (NIST) SRM 1950 Metabolites in Frozen Human Plasma using reverse phase LC-HRMS/MS. Lipid Annotator, LipidMatch, and MS-DIAL produced consensus annotations at the level of lipid class for 98% and 96% of features detected in positive and negative mode, respectively. Lipid Annotator provides percentages of fatty acyl constituent species and employs scoring algorithms based on probability theory, which is less subjective than the tolerance and weighted match scores commonly used by available software. Lipid Annotator enables analysis of large sample cohorts and improves data-processing throughput as compared to previous lipidomics software.
Identifiants
pubmed: 32178227
pii: metabo10030101
doi: 10.3390/metabo10030101
pmc: PMC7142889
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : NIAAA NIH HHS
ID : R24 AA022057
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States
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