Lipidomics as a discipline has seen a steady increase in research output throughout the last decade. With the advent of high-throughput metabolomics-platforms based on chromatography and high-resolution mass spectrometry, the need has increased for a central, well connected, and comprehensive resource for both experimental and computational scientists.
With LipidCompass, we want to offer a FAIR resource to simplify the exploration of quantitative and semi-quantitative lipidomics data from different angles and help establish a collection of core lipidomes for different organisms, tissues, and cell types, starting with eukaryotic model organisms. This is achieved using semantically enriched data, using controlled vocabularies and ontologies like NCIT, PSI-MS, NCBITaxon and lipid names following the structural hierarchy induced by the shorthand nomenclature for lipids. Lipidomics and Metabolomics tools that support mzTab-M as an output format can submit their data to LipidCompass. With automated mapping of lipid shorthand names to LIPID MAPS, SwissLipids and ChEBI, we open the possibility to integrate lipidomics data in a larger, systems biology context. LipidCompass provides comprehensive data exploration, comparison and interactive visualization features that simplify the detection of differences between samples within the same study, but, for the first time, also allow analysis and visualization of similarities and differences on a large scale between studies and to compare datasets against established qualitative and quantitative baselines.
LipidCompass will be the central integration hub for multiple lipid-related web services and can serve as a comprehensive resource for the establishment of core lipidomes, capturing the expected composition and structural variability of eukaryotic lipidomes in a first step.
Further collaboration with the International Lipidomics Society interest groups on standardization and clinical lipidomics will integrate support with the upcoming lipidomics checklist.