Lipidomics bioinformatics

Yetukuri L, Katajamaa M, Medina-Gomez G, Seppanen-Laakso T, Vidal-Puig A, Oresic M. Bioinformatics strategies for lipidomics analysis characterization of obesity related hepatic steatosis. BMC Syst. Biol. 2007 1 12-26. http //www.lipidlibmry.co.uk/lipids.html... 

Bioinformatics approaches for the automated high-throughput processing and molecular modeling with lipidomics data. 

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. 

This approach was extensively validated by the comparison of individual simulated TAG species with those obtained from lipidomics analysis [61]. The simulated K parameters represent the relative contributions of the different DAG pools resulting from the PA, MAG, and PI (PC) pathways to TAG synthesis. Accordingly, the bioinformatic simulation provides a powerful vehicle to determine altered TAG biosynthesis pathways under pathophysiological conditions. 

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. 

Yetukuri LR. Bioinformatics approaches for the analysis of lipidomics data. Espoo, Finland VTT Publications 2010. 

Niemela et al., 2009. Bioinformatics and Computational method for lipidomics. Journal of Chromatography B, 877 2855-2862. 

---