Genomics is the forerunner of all omics technologies, first introduced in 1987 as the name of a new journal. It has since morphed into the interdisciplinary field of functional genomics that studies all of the genes of any person or organism. However, it would take the advent of the post-genomic era, following the completion of the Human Genome Project, to trigger the development of other omics technologies. Information about the human genome created the foundations for research that produced new omics technologies, such as transcriptomics, proteomics, epigenomics, metabolomics, etc.
Today, omics refers to an array of technologies used to measure and functionally characterise different biological molecules in cells or tissues. These technologies collectively provide a comprehensive view of an individual's genetic makeup that is critical to the development of personalised medicines. They drive the development of diagnostic and therapeutic approaches to diseases by providing insights into the complex interactions between multiple genetic and environmental factors during pathogenesis. In drug discovery and development, omics technologies contribute to the selection of the most potent targets and to the discovery of more targeted therapeutic interventions, such as monoclonal antibodies.
Genomics, the first omics discipline, is the detailed study of entire genomes, in contrast to the study of individual genes or variants. The rapid evolution of next-generation sequencing methodologies enables the acquisition and investigation of genome-scale data. Today, genome-wide association studies (GWAS) have become the gold standard to identify genomic variants associated with complex traits of interest.
Epigenomics is the analysis of epigenetic marks across the genome. Epigenomic studies explore heritable, reversible modifications of DNA and chromatin that do not change primary nucleotide sequences. Epigenome-wide association studies (EWAS) enable the examination of genome-wide epigenetic variants in order to detect differences that are statistically associated with phenotypes of interest.
Transcriptomics is the study of the transcriptome, the complete set of RNA transcripts produced by the genome, under specific circumstances or in a specific cell. With spatial transcriptomics, it is now possible to measure expression levels of all or most genes systematically throughout tissue space, thereby significantly advancing the scope of research in the life sciences.
Proteomics is the complete evaluation of the structure and function of proteins and provides valuable information on the identity, expression levels, and modification of proteins. Though the technology can provide high proteome coverage, and therefore a better understanding of the nature of an organism, it is complicated by the fact that protein expression changes based on time and environmental conditions.
Metabolomics is the systematic identification and quantification of the small molecule metabolic products of a biological system at a specific point in time. It has been one of the fastest developing “-omics” technologies of the past decade and, together with transcriptomics, plays an important part in identifying connections between genetic regulation, metabolite phenotyping and biomarkers identification.
Microbiomes are dynamic communities of microorganisms that exist in a particular environment. The human microbiome, the complete set of genes contained in the entire collection of microorganisms that live in the human body, is as unique as a person’s fingerprint. Microbiomics is the field in which all the microorganisms of a given community, a microbiota, are investigated together.
But there are several other types of omics methodologies, such as glycomics, lipidomics, toxicogenomics, etc., that are valuable in describing different molecules, events and dynamics that together constitute a biological system. However, the efficient integration of data from individual omics studies and methodologies has the potential to provide a more holistic view that is much more than a sum of the parts view of complex biological systems.
The development of multiple omics technologies over the years has enabled a multi-layered approach to biological and life science research. For instance, data from each omics layer can provide unique insights into the biological pathways or processes associated with a particular disease. However, the output of the analysis of discrete data types is often limited to correlations that reflect reactive rather than causative processes.
In recent years, biomedical research has increasingly shifted to a multiomics approach to gain new insights into biological events and processes. The integration of different omics data types under a multiomics approach, aka integrated omics, pan-omics, and trans-omics, facilitates the unified analysis, visualization and interpretation of biological processes. Compared to single omics analyses, this integrated approach affords a big picture view of the flow of information within a process or a system. Researchers can now have a more complete understanding of a disease, from the potential causative changes that lead to disease to the functional consequences and potential treatment targets.
The integrated analysis of all contemporaneous data from all research-relevant biomolecules within a cell simultaneously covers every step on the path from DNA to biological function. This makes it easier to understand activities at a molecular level, determine disease mechanisms more precisely and design more targeted interventions.
Today, the availability of intelligent bioinformatics tools makes it much easier to leverage the potential of multiomics data to create multidimensional hierarchical models of biological systems that open up new research perspectives. multiomics, when combined with advanced AI/ML-powered bioinformatics platforms, will be an enabler for predictive science.
However some challenges — related to data integration, scalability and innovation — still need to be addressed. But the field of multiomics continues to evolve and expand to address new opportunities and challenges in life sciences research.
With the emergence of single-cell genomics, researchers are increasingly combining layers of data from single-cell sequencing techniques in multiomics experiments. At the same time, the increasing use of spatial proteomics and spatial transcriptomics to study the dynamics of disease and development is paving the way for integrated spatial multiomics to merge these unique datasets and create a systems-biology view of the tissue microenvironment.
And with growing interest in the development of monoclonal antibodies (mAbs), multiomics methodologies are also finding applications in the antibody discovery and development process. One study is taking a combined omics and synthetic biology approach to enhance biologics production from CHO cells, currently the most widely used industrial expression system for the manufacture of nearly 70% of biopharmaceuticals including mAbs. Another study applied longitudinal multiomics profiling followed by the systematic analysis of multiomics data to create a baseline for quality optimization and control of mAb production.
So multiomics continues to evolve in terms of scope and sophistication. Combined with advanced bioinformatics and computational approaches, it will continue to play an important role in the investigation of complex biological processes.