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LipidBlast in silico tandem mass spectrometry database for lipid identification
Open accessAbstract
Current tandem mass spectral libraries for lipid annotations in metabolomics are limited in size and diversity. We provide a freely available computer-generated tandem mass spectral library of 212,516 spectra covering 119,200 compounds from 26 lipid compound classes, including phospholipids, glycerolipids, bacterial lipoglycans and plant glycolipids. We show platform independence by using tandem mass spectra from 40 different mass spectrometer types including low-resolution and high-resolution instruments.
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Lipidomics reveals a remarkable diversity of lipids in human plasma
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Stein, S. E., & Scott, D. R. (1994) · Journal of the American Society for Mass Spectrometry · doi:10.1016/1044-0305(94)87009-8 · Cited by 882
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Barupal, D. K., Fan, S., Wancewicz, B., Cajka, T., Sa, M., Showalter, M. R., Baillie, R., Tenenbaum, J. D., Louie, G., Initiative, A. D. N., et al. (2018) · Scientific Data · doi:10.1038/sdata.2018.263 · Cited by 65
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The HDL lipidome is widely remodeled by fast food versus Mediterranean diet in 4 days
Zhu, C., Sawrey-Kubicek, L., Beals, E., Hughes, R. L., Rhodes, C. H., Sacchi, R., & Zivkovic, A. M. (2019) · Metabolomics · doi:10.1007/s11306-019-1579-1 · Cited by 40
Multiplatform Lipid Analysis of the Brain of Aging Mice by Mass Spectrometry
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An iPSC-derived astrocyte model of fragile X syndrome exhibits dysregulated cholesterol homeostasis
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Metadata
- Authors
- Kind, T., Liu, K.-H., Lee, D. Y., DeFelice, B., Meissen, J. K., & Fiehn, O.
- Venue
- Nature Methods
- Published
- 2013-06-30
- Volume
- 10
- Issue
- 8
- Pages
- 755-758
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2037833102
- Cited by
- 981
- References
- 2
- FWCI
- 18.42
- Type
- journal article
- Language
- en
- Open access
- green
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