Bioinformatics in Lipidomics

Lipidomics Comprehensive Mass Spectrometry of Lipids, First Edition. Xianlin Han. 2016 John Wiley Sons, Inc. Published 2016 by John Wiley Sons, Inc. 

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Yang, K., Han, X. (2011) Accurate quantification of lipid species by electrospray ionization mass spectrometry - Meets a key challenge in lipidomics. Metabolites 1,21-40. Satten, G.A., Datta, S., Moura, H., Woolfitt, A.R., Carvalho Mda, G., Carlone, G.M., De, B.K., Pavlopoulos, A., Barr, J.R. (2004) Standardization and denoising algorithms for mass spectra to classify whole-organism bacterial specimens. Bioinformatics 20, 3128-3136. 

Although many systematic indices (e.g.. Lipid MAPS, Chemical Entries of Biological Interest (ChEBI), lUPAC International Chemical Identifiers (InChl), simplified molecular-input line entry system (SMILES)) were developed to list the chemical compounds, these indices (identifiers) can only be meaningful if the compound is totally identified. However, in practice, lipidomics analysis in many cases can only provide partial identification of lipid molecular structures at the current development of technology. Moreover, different lipidomics approaches provide different levels of stmctural identification of lipid species. Therefore, how to clearly express and report the information about the levels of identification for the structures of lipid species (which can be derived fi om MS analysis) is not only helpful for the readers but also important for bioinformatics and data communication. To this end, the analysis by shotgun lipidomics could be used as a typical example to explain these levels. Similar phenomena also exist in the analysis of lipid species employing LC-MS-based approaches. 

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