Computational approaches for systems metabolomics. (June 2016)

• Record Type: Journal Article
• Authors: Krumsiek, Jan; Bartel, Jörg; Theis, Fabian J
• Is Part Of: Current opinion in biotechnology. Volume 39(2016)
• Page Start: 198
• Page End: 206
• Publication Date: 2016-06
• Subjects: Biotechnology -- Periodicals; 660.6
• DOI: 10.1016/j.copbio.2016.04.009
• Languages: English
• ISSNs: 0958-1669

Abstract

Graphical abstract: Highlights: Metabolomics is now established in the systems biology and systems genetics fields. Metabolomics is considered to be closely connected to the phenotype. The analysis of multiple 'omics datasets is a major computational challenge. We give an overview of multi-omics methods in the context of metabolomics. This covers multivariate statistical methods, pathway methods and network approaches. 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.