PuSH - Publication Server of Helmholtz Zentrum München

Witting, M. ; Ruttkies, C.* ; Neumann, S.* ; Schmitt-Kopplin, P.

LipidFrag: Improving reliability of in silico fragmentation of lipids and application to the Caenorhabditis elegans lipidome.

PLoS ONE 12:e0172311 (2017)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Lipid identification is a major bottleneck in high-throughput lipidomics studies. However, tools for the analysis of lipid tandem MS spectra are rather limited. While the comparison against spectra in reference libraries is one of the preferred methods, these libraries are far from being complete. In order to improve identification rates, the in silico fragmentation tool MetFrag was combined with Lipid Maps and lipid-class specific classifiers which calculate probabilities for lipid class assignments. The resulting LipidFrag workflow was trained and evaluated on different commercially available lipid standard materials, measured with data dependent UPLC-Q-ToF-MS/MS acquisition. The automatic analysis was compared against manual MS/MS spectra interpretation. With the lipid class specific models, identification of the true positives was improved especially for cases where candidate lipids from different lipid classes had similar MetFrag scores by removing up to 56% of false positive results. This LipidFrag approach was then applied to MS/MS spectra of lipid extracts of the nematode Caenorhabditis elegans. Fragments explained by LipidFrag match known fragmentation pathways, e.g., neutral losses of lipid headgroups and fatty acid side chain fragments. Based on prediction models trained on standard lipid materials, high probabilities for correct annotations were achieved, which makes LipidFrag a good choice for automated lipid data analysis and reliability testing of lipid identifications.
Altmetric
Additional Metrics?
[➜Log in]
Edit extra informations Login
Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Tandem Mass-spectrometry; Data-dependent Acquisition; Shotgun Lipidomics; Electrospray-ionization; Identification; Database; Metabolomics
ISSN (print) / ISBN 1932-6203
Journal PLoS ONE
Quellenangaben Volume: 12, Issue: 3, Pages: , Article Number: e0172311 Supplement: ,
Publisher Public Library of Science (PLoS)
Publishing Place Lawrence, Kan.
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Research Unit Analytical BioGeoChemistry (BGC)