Integrative Gene Regulatory Network inference using multi-omics data

Biological network inference is of importance to understand underlying biological mechanisms. Gene regulatory networks describe molecular interactions of complex biological processes. Graph models are mainly used for gene regulatory networks, where nodes and edges represent genes and their regulations respectively. In the most research, the molecular interactions (edges) of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data. However, gene expression is a product of sequential interactions of DNA sequence variations, single nucleotide polymorphism, copy number variation, histone modifications, transcription factor, DNA methylation, and many other factors. There are high-throughput genomic data that measure the various biological processes. We call the multiple types of genomics data as ‘multi-omics data’. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that can incorporate multi-omics data and their interactions in the graph model of gene regulatory network. Copy number variation and DNA methylation were considered for multi-omics data in this paper. The proposed method, iGRN, was applied to the human brain data of psychiatric disorder. Through the experiments, iGRN showed its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference.