AUTHOR=Hawe Johann S. , Theis Fabian J. , Heinig Matthias 

TITLE=Inferring Interaction Networks From Multi-Omics Data

JOURNAL=Frontiers in Genetics

VOLUME=Volume 10 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2019.00535

DOI=10.3389/fgene.2019.00535

ISSN=1664-8021

ABSTRACT=A major goal in systems biology is a comprehensive description 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-tow-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 (proteomics, transcriptomics, etc.)  enable researchers to predict novel functional interactions between molecular species.
Moreover, these data allow to distinguish relevant functional from non-functional interactions in a specific biological context. 
However, integration of multi-omics data is not straight forward due to their heterogeneity.
So far, interaction networks have mostly been inferred from homogeneous functional data (e.g. gene co-expression networks) and novel methods are essential for inferring comprehensive regulatory networks across omics data types.

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.