multiomics

Multiomics is an integrative approach in biology and medicine that analyzes multiple layers of molecular information from the same biological sample to understand structure and function more comprehensively than any single domain can provide. The term encompasses a range of omics disciplines—genomics, transcriptomics, proteomics, metabolomics, epigenomics, glycomics, microbiomics, and others—and seeks to link genetic variation with downstream processes and phenotypic outcomes.

Technologies used in multiomics include next-generation sequencing for DNA, RNA, and epigenetic marks; mass spectrometry and affinity-based assays for proteins and metabolites; and imaging-based methods for spatial omics. Recent advances enable simultaneous or linked measurements at the bulk or single-cell level, allowing integration of diverse data types across samples, tissues, and time.

Data integration is central to multiomics. Analysts combine heterogeneous data using early (feature-level), intermediate (representation-level), or late (decision-level) integration, and either vertical integration (across omics for the same samples) or horizontal integration (across cohorts). Common approaches include network analysis, matrix factorization, Bayesian and probabilistic models, and machine learning frameworks such as multi-omics factor analysis and similarity network fusion.

Applications span biomedical research and precision medicine, including cancer subtyping and biomarker discovery, understanding complex diseases, and exploring host-microbiome interactions, as well as agricultural and environmental studies. Notable initiatives include cancer genomics projects and consortia that integrate genomic, epigenomic, transcriptomic, and proteomic data to identify molecular pathways and therapeutic targets.

Challenges include data heterogeneity, differing scales and missingness, batch effects, high dimensionality, data sharing and privacy, and the need for standardized data formats and reproducible workflows. Despite these challenges, multiomics is increasingly used to build holistic models of biological systems and to inform clinical decision-making and translational research.