title: Computational approaches for systems metabolomics.
abstract: Systems genetics is defined as the simultaneous assessment and analysis of multi-omics datasets. In the past few years, metabolomics has been established as a robust tool describing an important functional layer in this approach. The metabolome of a biological system represents an integrated state of genetic and environmental factors and has been referred to as a 'link between genotype and phenotype'. In this review, we summarize recent progresses in statistical analysis methods for metabolomics data in combination with other omics layers. We put a special focus on complex, multivariate statistical approaches as well as pathway-based and network-based analysis methods. Moreover, we outline current challenges and pitfalls of metabolomics-focused multi-omics analyses and discuss future steps for the field.
authors.affiliation: Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany; German Center for Diabetes Research (DZD e.V.), Germany.
authors.forename: Jan
authors.initials: J
authors.lastname: Krumsiek
authors.affiliation: Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany; German Center for Diabetes Research (DZD e.V.), Germany.
authors.forename: Jörg
authors.initials: J
authors.lastname: Bartel
authors.affiliation: Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany; Department of Mathematics, Technische Universität München, Garching, Germany. Electronic address: fabian.theis@helmholtz-muenchen.de.
authors.forename: Fabian J
authors.initials: FJ
authors.lastname: Theis
mesh_terms: D019295:Computational Biology / Q000379:methods*; D006801:Humans; D055442:Metabolome; D055432:Metabolomics / Q000379:methods*; D008954:Models, Biological; D049490:Systems Biology / Q000379:methods*