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MOTIVATION Establishing phospholipid identities in large lipidomic datasets is a labour-intensive process. Where genomics and proteomics capitalize on sequence-based signatures, glycerophospholipids lack easily definable molecular fingerprints. Carbon chain length, degree of unsaturation, linkage, and polar head group identity must be calculated from mass(More)
The capacity to predict and visualize all theoretically possible glycerophospholipid molecular identities present in lipidomic datasets is currently limited. To address this issue, we expanded the search-engine and compositional databases of the online Visualization and Phospholipid Identification (VaLID) bioinformatic tool to include the(More)
There is a paucity of bioinformatic tools for spectral analysis capable of assigning and visualizing molecular identities from mass spectrometry-derived structural information. Predicting phospholipid lipid identities is a labour intensive process given the extreme variability in structure based on permutations of only a few atomic 'building blocks'.(More)
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