Inferring interaction networks from multi-omics data
Hawe JS, Theis FJ, Heinig M (2019)
Publication Type: Journal article
Publication year: 2019
Journal
Book Volume: 10
Article Number: 535
Journal Issue: JUN
Abstract
A major goal in systems biology is a comprehensive description of the entirety of all complex interactions between different types of biomolecules-also referred to as the interactome-and how these interactions give rise to higher, cellular and organism level functions or diseases. Numerous efforts have been undertaken to define such interactomes experimentally, for example yeast-two-hybrid based protein-protein interaction networks or ChIP-seq based protein-DNA interactions for individual proteins. To complement these direct measurements, genome-scale quantitative multi-omics data (transcriptomics, proteomics, metabolomics, etc.) enable researchers to predict novel functional interactions between molecular species. Moreover, these data allow to distinguish relevant functional from non-functional interactions in specific biological contexts. However, integration of multi-omics data is not straight forward due to their heterogeneity. Numerous methods for the inference of interaction networks from homogeneous functional data exist, but with the advent of large-scale paired multi-omics data a new class of methods for inferring comprehensive networks across different molecular species began to emerge. Here we review state-of-the-art techniques for inferring the topology of interaction networks from functional multi-omics data, encompassing graphical models with multiple node types and quantitative-trait-loci (QTL) based approaches. In addition, we will discuss Bayesian aspects of network inference, which allow for leveraging already established biological information such as known protein-protein or protein-DNA interactions, to guide the inference process.
Involved external institutions
Germany (DE)How to cite
APA:
Hawe, J.S., Theis, F.J., & Heinig, M. (2019). Inferring interaction networks from multi-omics data. Frontiers in Genetics, 10(JUN). https://doi.org/10.3389/fgene.2019.00535
MLA:
Hawe, Johann S., Fabian J. Theis, and Matthias Heinig. "Inferring interaction networks from multi-omics data." Frontiers in Genetics 10.JUN (2019).
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