Bioinformatics and computational approaches applicable to lipidomics

Abstract

Lipidomics owes its emergence to technologies developed over the past decade that empowered us with the ability to detect and quantify hundreds of intact lipid molecular species in parallel. One of the biggest current challenges of lipidomics is the elucidation of important pathobiological phenomena from the integration of large amounts of new data becoming available. In this respect, development of lipidomics as a field bears many similarities to the emergence and progress of genomics. Initial excitement over the ability to measure the expression of large numbers of genes in parallel was soon overshadowed by concerns over the ability to reliably analyze and interpret the expression data. This led to active research in the area of bioinformatics, which led to improved statistical methods as well as to new approaches for the analysis of transcriptome data in the pathway context. This review covers recent bioinformatics developments of relevance to analysis and interpretation of analytical lipidomics data, with the focus primarily on practical aspects of lipid bioinformatics.

A lipidomic analysis approach to evaluate the response to cholesterol-lowering food intake

“…The drawback of linear mixed models is that they consider all lipids separately and thus ignore the complex correlation structure of lipidomics data (Orešič 2009a). Studying multivariate changes in metabolite profiles may be particularly relevant for lipidomics, since % concentration changes of lipids across different phenotypes are usually small and distributed across multiple lipid species (Orešič 2009b; Yetukuri et al 2007).…”

“…It should provide a powerful approach to better identify and understand the often subtle effects of dietary ingredients on human metabolism and health (Hunter 2009). Advances in nutritional metabolomics allow a more extensive analysis of the individual response to a nutritional intervention and a reliable assessment of the contribution of specific metabolites and metabolic pathways to physiological processes related to health and disease (Orešič 2009a; Lodge 2010; Müller and Kersten 2003). …”

“…The cholesterol-lowering efficacy of PS intake is well-known, but it is not clear whether this also leads to metabolic effects at the level of specific plasma lipids, which are in dynamic exchange between lipoproteins and peripheral membranes. Lipidomics has recently been applied to study lipoprotein metabolism in both health and disease states (Ekroos et al 2010; Kontush and Chapman 2010; Orešič 2009a, b) e.g. to evaluate (adverse) effects of lipid-modifying drug treatments like statins (Bergheanu et al 2008; Kaddurah-Daouk et al 2010; Laaksonen et al 2006).…”

Machine-Learned Data Structures of Lipid Marker Serum Concentrations in Multiple Sclerosis Patients Differ from Those in Healthy Subjects

“…Currently, a scientific focus centers on eicosanoids including hydroxyeicosatetraenoic acids [6], ceramides, and lyosophosphatidic acids [7,8]. Along with the increasing knowledge about lipid signaling emerging from contemporary molecular research, the acquired data become increasingly complex, which is acknowledged in proposals to implement bioinformatics methods in lipid research [9,10,11]. …”

Lipidomics Applications for Discovering Biomarkers of Diseases in Clinical Chemistry

“…A comprehensive discussion of bioinformatics approaches to lipidomics can be found in several reported reviews (Niemelä et al, 2009;Oresic, 2009;Yetukuri et al, 2008). These developments need appropriate computational bioinformatics approaches to overcome the bottleneck of identifying and quantifying lipids from the raw chromatographic and mass spectral data.…”