Bioinformatics approaches for the analysis of lipidomics data

dc.contributorAalto Universityen
dc.contributor.advisorOresic, Matej, Research Prof., VTT
dc.contributor.authorYetukuri, Laxmana Rao
dc.contributor.departmentDepartment of Biomedical Engineering and Computational Scienceen
dc.contributor.departmentLääketieteellisen tekniikan ja laskennallisen tieteen laitosfi
dc.contributor.schoolAalto-yliopiston teknillinen korkeakoulufi
dc.contributor.schoolInformaatio- ja luonnontieteiden tiedekuntafi
dc.contributor.supervisorKaski, Kimmo, Prof.
dc.description.abstractThe potential impact of lipid research has been increasingly realised both in disease treatment and prevention. Recent advances in soft ionization mass spectrometry (MS) such as electrospray ionization (ESI) have permitted parallel monitoring of several hundreds of lipids in a single experiment and thus facilitated lipidomics level studies. These advances, however, pose a greater challenge for bioinformaticians to handle massive amounts of information-rich MS data from modern analytical instruments in order to understand complex functions of lipids. The main aims of this thesis were to 1) develop bioinformatics approaches for lipid identification based on ultra performance liquid chromatography coupled to mass spectrometry (UPLC/MS) data, 2) predict the functional annotations for unidentified lipids, 3) understand the omics data in the context of pathways and 4) apply existing chemometric methods for exploratory data analysis as well as biomarker discovery. A bioinformatics strategy for the construction of lipid database for major classes of lipids is presented using simplified molecular input line entry system (SMILES) approach. The database was annotated with relevant information such as lipid names including short names, SMILES information, scores, molecular weight, monoisotopic mass, and isotope distribution. The database was tailored for UPLC/MS experiments by incorporating the information such as retention time range, adduct information and main fragments to screen for the potential lipids. This database information facilitated building experimental tandem mass spectrometry libraries for different biological tissues. Non-targeted metabolomics screening is often get plagued by the presence of unknown peaks and thus present an additional challenge for data interpretation. Multiple supervised classification methods were employed and compared for the functional prediction of class labels for unidentified lipids to facilitate exploratory analysis further as well as ease the identification process. As lipidomics goes beyond complete characterization of lipids, new strategies were developed to understand lipids in the context of pathways and thereby providing insights for the phenotype characterization. Chemometric methods such as principal component analysis (PCA) and partial least squares and discriminant analysis (PLS/DA) were utilised for exploratory analysis as well as biomarker discovery in the context of different disease phenotypes.en
dc.format.extentVerkkokirja (752 KB, 75 s.)
dc.identifier.isbn978-951-38-7403-2 (electronic)
dc.identifier.isbn978-951-38-7402-5 (printed)#8195;
dc.relation.haspart[Publication 1]: L. Yetukuri, M. Katajamaa, G. Medina-Gomez, T. Seppänen-Laakso, A. Vidal-Puig, and M. Orešič (2007) Bioinformatics strategies for lipidomics analysis: characterization of obesity related hepatic steatosis, BMC Syst. Biol. 1: 12. © 2007 by authors.en
dc.relation.haspart[Publication 2]: L. Yetukuri, J. Tikka, J. Hollmén, and M. Orešič (2010) Functional prediction of unidentified lipids using supervised classifiers, Metabolomics 6: 18-26.en
dc.relation.haspart[Publication 3]: L. Yetukuri, S. Söderlund, A. Koivuniemi, T. Seppänen-Laakso, P. S. Niemelä, M. Hyvönen, M.-R. Taskinen, I. Vattulainen, M. Jauhiainen, and M. Orešič (2010) Composition and lipid spatial distribution of High Density Lipoprotein particles in subjects with low and high HDL-cholesterol, J. Lipid Res. In press. © 2010 American Society for Biochemistry and Molecular Biology (ASBMB). By permission.en
dc.relation.haspart[Publication 4]: G. Medina-Gomez, L. Yetukuri, V. Velagapudi, M. Campbell, M. Blount, M. Jimenez-Linan, M. Ros, M. Orešič, and A. Vidal-Puig (2009) Adaptation and failure of pancreatic β cells in murine models with different degrees of metabolic syndrome, Dis. Model. Mech. 2: 582-592. © 2009 by authors.en
dc.relation.haspart[Publication 5]: G. Medina-Gomez, S. L. Gray, L. Yetukuri, K. Shimomura, S. Virtue, M. Campbell, R. K. Curtis, M. Jimenez-Linan, M. Blount, G. S. H. Yeo, M. Lopez, T. Seppänen-Laakso, F. M. Ashcroft, M. Orešič, and A. Vidal-Puig (2007) PPAR gamma 2 prevents lipotoxicity by controlling adipose tissue expandability and peripheral lipid metabolism, PLoS Genet. 3: e64. © 2007 by authors.en
dc.relation.haspart[Publication 6]: A. Kotronen, V. R. Velagapudi, L. Yetukuri, J. Westerbacka, R. Bergholm, K. Ekroos, J. Makkonen, M.-R. Taskinen, M. Orešič, and H. Yki-Järvinen (2009) Serum saturated fatty acids containing triacylglycerols are better markers of insulin resistance than total serum triacylglycerol concentrations, Diabetologia 52: 684-690.en
dc.relation.ispartofseriesVTT publications, 741en
dc.subject.keywordlipid pathwaysen
dc.subject.keywordhigh density lipoproteinsen
dc.subject.keywordk-nearest neighboursen
dc.subject.keywordliquid chromatography/mass spectrometryen
dc.subject.keywordprincipal component analysisen
dc.subject.keywordpartial least squares and discriminant analysisen
dc.subject.keywordobesity support vector machinesen
dc.titleBioinformatics approaches for the analysis of lipidomics dataen
dc.typeG5 Artikkeliväitöskirjafi
dc.type.ontasotVäitöskirja (artikkeli)fi
dc.type.ontasotDoctoral dissertation (article-based)en
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