| Literature DB >> 28471643 |
Laura Goracci1, Sara Tortorella1, Paolo Tiberi2, Roberto Maria Pellegrino1, Alessandra Di Veroli1, Aurora Valeri1, Gabriele Cruciani1.
Abstract
To date, the main limitations for LC-MS-based untargeted lipidomics reside in the lack of adequate computational and cheminformatics tools that are able to support the analysis of several thousands of species from biological samples, enabling data mining and automating lipid identification and external prediction processes. To address these issues, we developed Lipostar, novel vendor-neutral high-throughput software that effectively supports both targeted and untargeted LC-MS lipidomics, implementing data acquisition, user-friendly multivariate analysis (to be used for model generation and new sample predictions), and advanced lipid identification protocols that can work with or without the support of preformed lipid databases. Moreover, Lipostar integrates the lipidomic processes with a full metabolite identification (MetID) procedure, essential in drug safety applications and in translational studies. Case studies demonstrating a number of Lipostar features are also presented.Entities:
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Year: 2017 PMID: 28471643 DOI: 10.1021/acs.analchem.7b01259
Source DB: PubMed Journal: Anal Chem ISSN: 0003-2700 Impact factor: 6.986