How multiomics can be used to improve healthcare

• Multiomics involves analyzing diverse biological data to enhance our understanding of health and disease
• Combining data from fields like genomics, proteomics, and metabolomics is helping researchers personalize treatments, identify new drug targets, and diagnose diseases earlier
• Although technical and ethical hurdles remain, continued innovation in tools and frameworks is making multiomics increasingly accessible

Multiomics, also known as multiple omics or multiomic analysis, is rapidly changing our ability to understand and treat disease.^1,2

By combining data from fields such as genomics, transcriptomics, epigenetics, microbiomics, metabolomics, and proteomics, multiomics has enabled a more thorough understanding of the molecular changes that drive cellular development and disease onset.^1,2

This holistic approach to biological data analysis is empowering researchers and healthcare personnel to not only accelerate precision medicine, but also to uncover novel therapeutic targets and identify certain diseases earlier.³

Here, we take a closer look at the various benefits multiomics can provide, highlighting how it can drive progress in the healthcare industry.

Multiomics involves the simultaneous analysis of two or more types of “omics”-led data to deepen the understanding of complex biological processes and systems.^1,6 This can include data from fields such as:

• Genomics – the identification of genes and genetic variants associated with disease¹
• Epigenomics – the analysis of modifications in DNA or DNA-associated proteins¹
• Transcriptomics – the study of the transcriptome (the entire collection of RNA transcripts) within a cell, tissue, or organism⁴
• Proteomics – the identification of protein levels and interactions at the genome level¹
• Metabolomics – the analysis of all metabolites present within a cell, tissue, or organism¹
• Microbiomics – the investigation of all microorganisms within a given microbial community⁵

For example, scientists can now link genetic variations to observable traits and functional biological changes — insights that prove critical when looking to adopt a more patient-centric approach. Treatments can become tailored therapies addressing each patient’s unique molecular makeup.^3,7

Multiomics can help identify new therapeutic targets and enhance treatment precision across a wide range of therapeutic areas. 

Within the field of oncology, multiomic analysis is already enabling researchers to classify tumors at a molecular level, leading to more effective interventions.^8,9

A notable example is the Cancer Genome Atlas Program, launched in 2006, which involved analyzing over 20,000 primary cancer samples across 33 cancer types.⁸ Over the following 12 years, the program helped generate more than 2.5 petabytes of genomic, epigenomic, transcriptomic, and proteomic data. These insights have contributed to more accurate cancer classification and improved prognosis and treatment strategies.⁹

Multiomics is also helping researchers identify, design, and develop drug treatments with fewer side effects. By pinpointing specific molecules or pathways, multiomic approaches can reveal new potential targets for drug intervention.¹⁰

At the same time, multiomics supports the discovery of biomarkers, allowing scientists to monitor the progression of certain diseases more easily and predict a patient’s response to treatment with greater accuracy.¹¹

Multiomics has advanced significantly since 2003 — the year the first sequence of the human genome was generated.¹² Since then, it has contributed to a number of developments throughout the healthcare industry, including: 

• More accurate cancer diagnoses — the Cancer Genome Atlas Program demonstrated that analyzing molecular disease pathways allows patients to be grouped more precisely, leading to improved prognoses and more targeted treatment options⁹

• Detailed insights during COVID-19 — multiomic studies during the COVID-19 pandemic helped reveal why some individuals experienced more severe illness than others. By being able to assess distinct immune profiles and metabolic changes, researchers were able to inform the development of various vaccines¹³

• Improved diagnosis of rare diseases — the integration of multiomics has enabled clinicians to identify disease-causing mutations in patients with previously undiagnosed conditions, including rare neurological diseases¹⁴

• The integration of personalized medicine — thanks to the advances in predictive health models powered by multiomics, researchers are discovering biomarkers capable of forecasting conditions like type 2 diabetes years before symptoms appear¹⁵

There are various multiomics technologies, each enabling researchers to sequence, extract, and process biological information in multiple ways. Some of the most common examples of these technologies include:^16–18

• Next-generation sequencing (NGS) — a high-throughput method used to rapidly sequence DNA or RNA

• Mass spectrometry (MS) — an analytical technique used to identify and quantify proteins and metabolites

• Single-cell sequencing (SSS) — a method that delivers high-resolution insights into cellular diversity and disease mechanisms

• Artificial intelligence (AI) and machine learning (ML) — advanced data-led approaches used to integrate and analyze complex datasets to identify specific patterns and behaviors

The biological insights that multiomic approaches can provide wouldn’t exist without the sequencing techniques specific to each “omic” field. Each technique focuses on analyzing a particular biological layer — whether that be the genome, transcriptome, or microbiome — before being integrated to form a more complete picture.^1,2

As the name suggests, whole genome sequencing captures the entire genomic sequence of an organism, offering a genetic blueprint that allows researchers to identify any potential mutations.¹⁹

Meanwhile, RNA sequencing enables scientists to specifically profile the transcriptome, providing insights into how genes are expressed under different conditions.²⁰

Other multiomic sequencing methods also play a crucial role. Microbiome sequencing, for instance, helps deliver detailed insights into microbial community interactions and their potential influence on various diseases.²¹

By combining these approaches, researchers can deliver a more layered and holistic assessment based on diverse datasets. This integration is now enabling the identification of more accurate biomarkers, the delivery of personalized care to patients, and the development of targeted treatment options.²¹

While multiomics has already provided the healthcare landscape with various benefits, several areas still require some additional time and investment, largely in terms of costs and ethical considerations.

We’ve seen how multiomics can create a more layered approach to understanding health and disease, but putting this into practice isn’t always straightforward. In fact, bringing these different types of data together remains a complex task.²²

Since each dataset has its own unique format, powerful computational tools are often needed to convert this information into a usable language.²² While continuous progress is being made, these processes can still be time-consuming and require thoughtful coordination to ensure researchers can manage and interpret multi-layered data efficiently.

For multiomics to be truly effective within healthcare, it requires more than just access to the right data — it also depends on having the right infrastructure and equipment in place.²³  Performing multiomic analysis calls for powerful computational tools, robust data storage solutions, and standardized workflows.²³ While these systems are becoming more advanced and accessible, they can still represent a significant investment, especially when aiming to scale and integrate them into routine clinical practice.

Since multiomics is a relatively new field that involves multiple techniques, the regulatory landscape surrounding it can be complex. To ensure clinical compliance, researchers must follow specific guidelines that apply not only to each omics discipline but also to the sequencing technologies and analytical tools being used.

For example, when using single-cell sequencing, tools like GLUE — graph-linked unified embedding — can help integrate diverse datasets, supporting results that are both more scientifically robust and aligned with regulatory standards.²⁴

Integrating multiomics into patient care brings important ethical considerations, largely in terms of patient privacy, data security, and informed consent.

Since multiomics involves the collection and analysis of extensive personal biological information, it’s vital to ensure that these data are handled responsibly and securely, without compromising patient confidentiality. Moving forward, this will likely require the development of comprehensive ethical guidelines and the adoption of blockchain technology — a transparent database mechanism designed to store and distribute data securely.²⁴

As healthcare and technology continue to evolve, advances in multiomics are helping us unlock a deeper, more connected understanding of human biology. By combining data from fields like genomics and proteomics, we are rapidly moving toward a landscape where earlier diagnoses and more precise treatments are fast becoming a reality.