LAUSR

Highlights • Single-level data analysis produced by high-throughput technologies is limited by showing only a narrow window of cellular functions.• Data integration across different platforms, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, provides opportunities to understand causal relationships across multiple levels of cellular organization.• We review some of the most frequently used frameworks for multi-omics data integration.• We consider the significance of multi-omics in the functional identification of driver genomic alterations and discuss methods developed to exploit associations between mutations and downstream signaling pathways.• We provide an overview of the utility of multi-omics in tumor classifications, prognostications, diagnostics, and the role of data integration in the quest for novel biomarkers and therapeutic opportunities.• Translation of multi-omics technologies into tools accessible in daily medical routine is slow. One major obstacle is the uneven maturity of different omics approaches for routine clinical applications.