Multiomic

Multiomics, sometimes described as multiomic analyses, refers to the integrative analysis of data produced by multiple omics platforms to study biological systems. Omics are broad categories such as genomics (DNA sequence and variation), epigenomics (DNA accessibility and modifications), transcriptomics (RNA expression), proteomics (protein abundance and modification), metabolomics (small-molecule metabolites), lipidomics, glycomics, and microbiomics, among others. By combining these layers, researchers aim to capture causal relationships and regulatory networks that are not apparent from a single modality alone. Multiomics can be applied to bulk tissues, individual cells, or microbial communities, and is a key component of systems biology.

Study designs include collecting matched samples across modalities and applying computational integration methods. Approaches range from early integration, where data matrices are merged before analysis, to intermediate methods such as matrix factorization and network-based fusion, to late integration where modality-specific models are combined at the decision stage. Common computational strategies include multi-view learning, Bayesian data fusion, kernel-based methods, and graph or network analyses. Single-cell multiomics extends this concept by measuring two or more modalities in the same cell, improving resolution of cellular states and regulatory mechanisms. Challenges include differences in data scale and noise, batch effects, missing data, and the need for standardized pipelines and data sharing.

Applications span precision medicine, oncology, developmental biology, neuroscience, microbial ecology, and agriculture. In cancer, integrating genomic alterations with transcriptomic and proteomic profiles can reveal therapeutic targets and resistance mechanisms. In microbiome studies, combining metagenomics with metabolomics links microbial composition to functional outputs. In plants, multiomic profiling is used for trait discovery and stress responses. Despite rapid progress, widespread adoption requires improved experimental design, cost reductions, and robust statistical methods.