Multiomics has moved mainstream, integrating genomics, proteomics, transcriptomics, and metabolomics to provide a holistic view of biology and disease.
Genomics now underpins clinical care, linking 6,000+ genes to ~7,000 disorders and enabling rare disease diagnosis, therapy targeting, and precision medicine.
Global disparities remain — local sequencing efforts and public–private collaborations are vital to capture population-specific variants.
Multiomic data integration is powering next-generation therapies like CRISPR-based gene editing and personalized monitoring through biomarkers.
Single-cell multiomics now reveals genomic, transcriptomic, and epigenomic changes within the same cell, driving insights into tissue biology.
AI and long-read sequencing are key to integrating complex single-cell and spatial datasets for deeper biological understanding.
Data infrastructure is lagging — researchers need purpose-built, federated computing platforms and unified analytical pipelines for multiomic datasets.
Network-based integration maps omics data onto biochemical pathways, enabling precise identification of dysregulated reactions and drug targets.
Clinical applications are expanding, with multiomics improving disease prediction, stratification, and therapy optimization, especially in oncology.
Future success depends on standardization, collaboration, and inclusion, ensuring reproducibility, computational scalability, and equitable access to genomic medicine.
