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Wörheide, M. ; Krumsiek, J.* ; Kastenmüller, G. ; Arnold, M.

Multi-omics integration in biomedical research – A metabolomics-centric review.

Anal. Chim. Acta 1141, 144-162 (2021)
Postprint DOI
Open Access Green
Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Korrespondenzautor
Schlagwörter Data Integration ; Lipidomics ; Metabolomics ; Multi-omics ; Systems Biology; Genome-wide Association; Principal Component Analysis; Constraint-based Models; Global Reconstruction; Cellular-metabolism; Alzheimers-disease; Expression; Atlas; Knowledgebase; Visualization
ISSN (print) / ISBN 0003-2670
e-ISSN 1873-4324
Zeitschrift Analytica Chimica Acta
Quellenangaben Band: 1141, Heft: , Seiten: 144-162 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Radarweg 29, 1043 Nx Amsterdam, Netherlands
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen National Institutes of Health/the National Institute on Aging (NIA), USA