Research Article Predicting Glycerophosphoinositol Identities in Lipidomic Datasets Using VaLID (Visualization and Phospholipid Identification)—An Online Bioinformatic Search Engine Graeme S. V. McDowell, 1,2,3 Alexandre P. Blanchard, 1,2,3 Graeme P. Taylor, 1,2 Daniel Figeys, 2,3 Stephen Fai, 3,4 and Steffany A. L. Bennett 1,2,3 1 Neural Regeneration Laboratory, Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, ON, Canada K1H 8M5 2 Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, ON, Canada K1H 8M5 3 CIHR Training Program in Neurodegenerative Lipidomics, Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, ON, Canada K1H 8M5 4 Carleton Immersive Media Studio, Azrieli School of Architecture and Urbanism, Carleton University, ON, Canada K1S 5B6 Correspondence should be addressed to Stefany A. L. Bennett; sbennet@uottawa.ca Received 6 November 2013; Accepted 23 December 2013; Published 20 February 2014 Academic Editor: Tao Huang Copyright © 2014 Graeme S. V. McDowell et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. he 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 Identiication (VaLID) bioinformatic tool to include the glycerophosphoinositol superfamily. VaLID v1.0.0 originally allowed exact and average mass libraries of 736,584 individual species from eight phospholipid classes: glycerophosphates, glyceropyrophosphates, glycerophosphocholines, glycerophosphoethanolamines, glycerophosphoglycerols, glycerophosphoglycerophosphates, glycerophosphoserines, and cytidine 5  -diphosphate 1,2-diacyl-sn-glycerols to be searched for any mass to charge value (with adjustable tolerance levels) under a variety of mass spectrometry conditions. Here, we describe an update that now includes all possible glycerophosphoinositols, glycerophosphoinositol monophosphates, glycerophosphoinositol bisphosphates, and glycerophosphoinositol trisphosphates. his update expands the total number of lipid species represented in the VaLID v2.0.0 database to 1,473,168 phospholipids. Each phospholipid can be generated in skeletal representation. A subset of species curated by the Canadian Institutes of Health Research Training Program in Neurodegenerative Lipidomics (CTPNL) team is provided as an array of high-resolution structures. VaLID is freely available and responds to all users through the CTPNL resources web site. 1. Introduction he emerging ield of lipidomics seeks to answer two seem- ingly simple questions: How many lipid species are there? What efect does lipid diversity have on cellular function? To address these questions, lipidomics requires a comprehensive assessment of cellular, regional, and systemic lipid home- ostasis. his assessment expands beyond lipid proiling to include the transcriptomes and proteomes of lipid metabolic enzymes and transporters, as well as that of the protein targets that afect downstream lipid signalling [1]. Lipidomic analyses also encompass an unbiased mechanistic assessment of lipid function ranging from the physicochemical basis of lipid behaviour to lipid-protein and lipid-lipid interactions triggered by intrinsic and extrinsic stimuli [1]. he irst step, however, lies in identifying the molecular identities of the lipid constituents in diferent membrane compart- ments. Hindawi Publishing Corporation BioMed Research International Volume 2014, Article ID 818670, 8 pages http://dx.doi.org/10.1155/2014/818670