The Omics Breakdown: How Each Field Reveals a Different Layer of Biology

By Dr. Priyom Bose, Ph.D.Reviewed by Lauren Hardaker

Traditional analytical approaches often fail to uncover the complexities of biological systems involving many biomolecules. In contrast, omics, an interdisciplinary approach, focuses on understanding the interactions within biological processes by considering the whole system instead of targeting individual components. 

Image credit: vchal/Shutterstock.com

The concept of multi-omics encompasses the subfields of proteomics, transcriptomics, epigenomics, microbiomics, glycomics, genomics, glycoproteomics, lipidomics, metabolomics, and several others.¹ The combined analysis of various omics layers reveals a more detailed and precise understanding of how biological systems function.

Genomics And Transcriptomics

Unlike genetics, which focuses on a single gene, genomics involves the study of the entire genome, helping to uncover the complex interactions between genes and their roles in biological processes. Furthermore, understanding the variations in the genomic structure sheds light on inherited diseases and differences between individuals.² Genomics has played a crucial role in the pharmaceutical industry due to the rapid identification of protein targets and has accelerated the process of drug discovery.

Technological innovations have cost-effectively improved the throughput and accuracy of sequencing capabilities. These innovations have significantly impacted the identification of coding and regulatory regions and the understanding of the spatial organization of DNA within a cell nucleus.

Transcriptomics is the study of gene expression patterns in an individual's entire set of RNA molecules.³ High-throughput sequencing technologies can quantify the frequency of a specific sequence in the transcriptome. In transcriptomic analysis, researchers isolate RNA from a tissue sample and analyze messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), small RNAs (sRNAs), and other types of RNA. This process provides a representation of gene expression across a population of cells.

Recently, long-read sequencing techniques, such as Oxford Nanopore or PacBio Iso-Seq, have enabled the sequencing of complete transcripts to detect multiple isoforms from the same gene. Furthermore, spatial transcriptomics (ST), which uses fluorescence in situ hybridization and sequencing-based methods, facilitates high-resolution measurement of gene expression in intact tissues.⁴ Spatial omics approaches now extend beyond RNA to include spatial proteomics, using imaging mass spectrometry and multiplexed antibody methods, to map proteins within tissue architecture.

Epigenomics

Epigenomics explores heritable but reversible modifications to DNA and histones that regulate gene activity without altering the underlying sequence. DNA methylation, histone modifications, and chromatin accessibility are commonly profiled using technologies such as bisulfite sequencing, ATAC-seq, and ChIP-seq.⁵ Epigenomic patterns help to explain how environmental exposures, aging, and disease states shape cellular behavior beyond what is encoded in the genome. For example, large-scale epigenome-wide association studies (EWAS) have identified methylation signatures linked to cancer risk and metabolic disorders.⁶

Epigenome: The symphony in your cells. Video credit: NatureVideoChannel/Youtube.com

Proteomics And Glycoproteomics

Proteomics and glycoproteomics focus on explaining the relationships between protein structure and function. Post-translational modifications and protein abundance are commonly explored to understand the dynamic behavior of proteins. Mass spectrometry (MS)-based proteomic analyses identify proteins via a bottom-up or top-down approach.⁷

Both bottom-up and top-down MS strategies are important for characterizing protein isoforms and interpreting post-translational modifications, such as acetylation, glycosylation, and phosphorylation. Researchers perform proteomics analysis on various tissue types, including extracellular vesicular exosomes (EVs), nucleosomes, and bodily fluids (e.g., plasma, serum, and cerebrospinal fluid).

Glycomics, distinct from glycoproteomics, refers to the study of the entire range of glycans in a cell or organism, regardless of whether they are free or attached to proteins or lipids.⁸ Glycomics provides insights into immune modulation, host-pathogen interactions, and cancer biomarkers.

Glycoproteomics MS-based analyses primarily focus on a comprehensive study of the carbohydrate moieties of glycoproteins. This omic branch sheds light on the structure and functional dynamics of glycans attached to lipids and proteins across various biological systems and their role in health and disease. Glycoproteomics analysis offers insights into immune responses, cellular communication, disease progression, and pathogen interactions. High-performance liquid chromatography–mass spectrometry (LC-MS) is generally employed to uncover the complex structures of glycans and the protein O- and N-site heterogeneity.

Metabolomics And Lipidomics

The metabolome is the complete collection of small-molecule metabolites within a biological sample at a specific time. Metabolomics involves the comprehensive analysis of small-molecule metabolites within a biological sample. Understanding the mechanism of action of metabolites is essential, as they play a crucial role in various biochemical pathways and cellular functions through both stimulatory and inhibitory actions in both healthy and diseased states.⁹ For instance, cancer cells modify their metabolic pathways to adapt to tumor microenvironments and survive pharmacological treatments.

Single-cell metabolomics (SCM) has been developed, including Patch-clamp-based nano electrospray ionization (ESI)-MS, capillary electrophoresis-MS (CE-MS), matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI), and mass cytometry.¹⁰ Researchers use quantitative metabolomics to investigate neurodegenerative disorders and cancer. For instance, metabolomics applied to a preclinical model of Alzheimer’s disease (AD) led to the identification of seven AD-related neurotransmitters and sixteen biomarkers.

Lipids are hydrophobic, structurally diverse molecules involved in intra- and extracellular signaling processes and multiple biological functions within the cell. The nervous system contains a significant amount of lipids, and several neurological disorders may arise due to disturbances in lipid homeostasis. Lipidomics is a large-scale study of lipids or lipidomes within cells, tissues, or organisms.¹¹

Researchers have applied lipidomics to the pathological characteristics of AD and Parkinson’s disease. They also observed characteristic alterations in lipidome profiles in rats and mice with spinal cord and sciatic nerve injury. These findings suggest that lipid metabolism plays a crucial role in the recovery processes following nerve injury. Recent lipidomics studies have also identified lipid biomarkers predictive of cardiovascular disease and metabolic syndrome.¹²

Microbiomics

Microbiomics investigates the collective genomes and functional capacity of microbial communities associated with humans, animals, plants, and environments. Metagenomic sequencing and metatranscriptomics are widely used to characterize microbial diversity and activity.¹³ The human gut microbiome, in particular, has been linked to immune function, metabolic health, and therapy response. Microbiome profiling has become increasingly relevant in oncology, where microbial composition can influence patient response to immunotherapy.¹⁴

Multi-Omics Data Integration

Data integration is a process in which different omics datasets, from the same set of samples on a genome-wide scale, are combined.¹⁵ This process allows scientists to combine multiple layers of biological insights to interpret the biological process. Multi-omics profiling quantifies various biological signals and explores the intricacies of interconnections between multiple layers of biological molecules, identifying system-level biomarkers.

The optimal data integration process depends on several factors: data type, size, quality, and resolution. Even the impacts of organisms and tissues may influence the bioinformatic tools used for the process. Researchers have trouble accurately extracting true biological networks due to the complexity and diversity of multi-omic datasets. Large-scale multi-omics data are often generated across platforms and laboratories, which creates additional unwanted variations.

Efficient data integration is essential for reliable multi-omics profiling. Both horizontal and vertical (cross-omics) integration methods have been designed for effective multi-omic profiling. Quality control processes should be performed throughout the entire sample-to-result pipeline to ensure effective data integration.

Multiomics is changing the game - hear from researchers using it. Video credit: Illumina/Youtube.com

References

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Last Updated: Oct 1, 2025