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Fields such as proteomics have a long history of providing databases of proteins, with standardised naming formats and intelligent mappings that relate protein names in different resources. Key to this type of research is the existence of a centralized namespace against which to identify the molecules. This is partially due to several reasons such as the technical improvements in the mass spectrometers and the improved availability of reagents and synthetic standards necessary for quantitative lipidomics.Ĭentral to all “omics” disciplines including lipidomics is the high-throughput identification of biological molecules followed by their accurate description and reporting. The first high throughput lipidomics MS publications emerged in 1994, and have since gained considerable momentum with dozens of papers published per year. The workhorse of high throughput lipidomics is mass spectrometry (MS), often generating hundreds of lipid identifications in a single experiment. Perturbations to the lipidome have been reported in several human diseases such as diabetes, obesity, Alzheimer’s disease, liver disease, hypertension and schizophrenia. They have wide ranging functions across the entire breadth of biological kingdoms, including roles in the structure of cell membranes, energy storage and cell signalling. Lipids are a broad class of molecules with a variety of structures. ) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist.Ī key area of metabolomics research is performed in the field of lipidomics, which deals with the detection and identification of lipids, fatty acids and derivatives. PM is supported by an EMBL Student Fellowship. JAV is also supported by the EU FP7 grant ProteomeXchange and by the Wellcome Trust. MJOW, AF and JAV are supported by the EU FP7 grant LipidomicNet. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.įunding: JMF is supported by a BBSRC CASE studentship funded by the BBSRC and Philips. Received: JanuAccepted: MaPublished: May 7, 2013Ĭopyright: © 2013 Foster et al. PLoS ONE 8(5):Įditor: Matej Oresic, Governmental Technical Research Centre of Finland, Finland (2013) LipidHome: A Database of Theoretical Lipids Optimized for High Throughput Mass Spectrometry Lipidomics. Ĭitation: Foster JM, Moreno P, Fabregat A, Hermjakob H, Steinbeck C, Apweiler R, et al. The web application encompasses a browser for viewing lipid records and a ‘tools’ section where an MS1 search engine is currently implemented. Additionally, cross-references to other lipid related resources and papers that cite specific lipids were used to annotate lipid records. Designed specifically to accommodate high throughput mass spectrometry based approaches, lipids are organised into a hierarchy that reflects the variety in the structural resolution of lipid identifications. In parallel, a web application was developed to present the information and provide computational access via a web service. Using the ‘FASTLipid’ Java library, a database was populated with theoretical lipids, generated from a set of community agreed upon chemical bounds. This work aims to reduce the gap by developing an equivalent resource to UniProt called ‘LipidHome’, providing theoretically generated lipid molecules and useful metadata. While having a seasoned community of wet lab scientists, lipidomics lies significantly behind proteomics in the adoption of data standards and other core bioinformatics concepts. Taken largely for granted, similar mature resources such as UniProt are not available yet in some other “omics” fields, lipidomics being one of them. One example of such resources is UniProt, enriched with both expertly curated and automatic annotations. Protein sequence databases are the pillar upon which modern proteomics is supported, representing a stable reference space of predicted and validated proteins.