Multi-omics data integration for topology-based pathway activation assessment and personalized drug ranking

Abstract

Although multi-omics analysis is popular for revealing diverse physiological effects and biomarkers in many branches of state-of-the-art molecular and cell biology and bioinformatics, there is still no consensus on a gold standard protocol for the integration of various multi-omics profiles into a uniformly shaped system bioinformatics platform. In the current study, we performed the integration of data on DNA methylation, and the expression of coding RNA (mRNA), micro-RNA (miRNA), and long non-coding RNA into a joint platform for calculation of signaling pathway impact analysis (SPIA) and drug efficiency index (DEI). We found that the mirrored and balanced DEI values fitted the DNA methylome data better than the original DEI. Additionally, the protein-coding mRNA-based values correlated more strongly with antisense lncRNA-based values than with miRNA-based values. The whole correlation between the mRNA-based and antisense lncRNA-based values was generally positive. This platform allowed integrative analysis of several levels of gene expression regulation of protein-coding genes and their regulators, including methylation and noncoding RNAs.