How Multiomics Enables Integrated Biological Analysis

This article was reviewed by Joe Cursons, PhD, from immuno-oncology startup oNKo-innate.

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Conceptual image of multiomics: Biological samples such as fish and apples are surrounded by scientific instruments like flasks, test tubes, and a microscope, with strands of DNA and different chemical molecules — Multiomics allows scientists to create an integrated understanding of complex biological systems.
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Since the advent of the Human Genome Project, scientists have expanded efforts to map complete biological datasets that encompass entire molecular families, such as DNA, RNA, proteins, and metabolites, through omics research. Increasingly, scientists turn to multiomics approaches that integrate various omics methods to see biology’s full, multilayered, and complex picture. In this article, explore how researchers integrate multiomics data from the same sample to gain unique insights, from genotype to phenotype.

What Is Multiomics?

Multiomics is a multidisciplinary, holistic approach to biological analysis in which scientists integrate and analyze different types of omics data from a single sample.¹ The omes commonly featured in multiomics analysis include the genome, epigenome, transcriptome, proteome, metabolome, and microbiome.²

In a multiomics analysis, DNA molecules can be sequenced to generate the genome, epigenome, and microbiome; RNA sequencing yields the transcriptome; and molecular approaches such as mass spectrometry or NMR provide information on proteins in the proteome and metabolites in the metabolome.

Researchers collect different data types through multiomics analysis and generate insights into the genome, epigenome, microbiome, transcriptome, proteome, and metabolome with analyte-specific methodologies.

Modified from © istock.com , Iconic Prototype , Trinset, VectorMine; designed by Erin Lemieux

Types of Omics

There are many types of omics sets that multiomics may encompass. This includes the following.

Genomics
Epigenomics
Transcriptomics
Proteomics
Metabolomics and lipidomics
Metagenomics and microbiomics
Glycomics
Spatial omics

Each of these standalone omics technologies offers detailed insight into a particular group of molecules, such as DNA, RNA, or proteins, but has a restricted view of the entire biological system.¹ By combining two or more of these large-scale omics datasets, scientists can form a comprehensive understanding of the biological complexity of the sample and explore relationships between the different omes.

“The collection and comparison of different types of omics data allows us to look at, for example, whether there are any interactions between changes in the transcription of [a gene] and the changes in the abundance of the corresponding protein, or whether changes in histone modifications at a certain site in the DNA correspond to changes in the transcription of that target gene,” explained Joe Cursons, a computational biologist at immuno-oncology startup oNKo-innate, who uses multiomics to understand the immune response to solid tumors.

Multiomics Approaches

Approaches to multiomics analysis differ considerably based on which omes are included and whether the samples are individual cells or larger tissues. Recent advances in sequencing methods have allowed scientists to study multiomics at the single-cell level,³ analyzing minute quantities of various molecules. In contrast, spatial multiomics enables them to study the different omes in the context of their native tissues.⁴

For example, a researcher might perform a multiomics workflow to compare the genome, epigenome, transcriptome, and proteome of a muscle tissue sample from a patient with muscular dystrophy. To do so, they could split a muscle tissue biopsy into four smaller samples and perform the following experiments to generate four different omics datasets.

Sample 1 – Genome: Extract DNA and perform whole-genome sequencing
Sample 2 – Epigenome: Extract DNA for bisulfite sequencing of DNA methylation patterns and nanopore sequencing of histone modifications
Sample 3 – Transcriptome: Extract RNA to execute RNA sequencing
Sample 4 – Proteome: Isolate proteins and subject them to liquid chromatography-mass spectrometry (LC-MS) analysis

Several commercial sequencing methods used in multiomics research can perform more than one type of omics analysis simultaneously, including ATAC-seq, which simultaneously profiles the transcriptome and epigenome,⁵ and CITE-seq, which sequences the transcriptome and proteome.⁶ “A big contributor to the increase in multiomics recently has been the growing availability of these commercial kits,” said Cursons. Scientists also use 16S rRNA gene sequencing to study the diversity of microbes that live symbiotically or pathogenically in human tissues, making up the microbiome.²

Multiomics Analysis

Researchers perform data integration through bioinformatics and computational biology pipelines, analyzing different omics datasets generated by high-throughput technologies. Multiomics researchers typically analyze data independently using programs such as Python or R to avoid the shortcomings of summary statistics from individual omics pipelines.

“A critical consideration with multiomics is that you really need to plot the data [yourself],” Cursons explained. “Quite often, the different measurement technologies that you're using for these different types of omics will have very different sources of noise, and the best statistical models for that noise might vary between different types of data.” Because of the complexity, heterogeneity, and scale of multiomics data, machine learning tools are becoming increasingly common in multiomics analysis.⁷

In the example of muscular dystrophy, this integrated multiomics approach allows researchers to explore the molecular continuum from gene to protein, revealing complex mechanisms and pathways that underpin the muscular dystrophy phenotype and identifying potential targets for treatment.

Targets identified in multiomics analyses typically require further experiments to validate them or confirm causality, such as gene knockouts. “It's sometimes quite hard with multiomics methods to necessarily identify the cause and effect,” remarked Cursons. “What's the chicken, and what's the egg? Is it the changes in the transcription causing the changes in the histone markers? Or is it the changes in the histone markers changing the gene expression? That sometimes gets a bit blurry unless you go in and do a perturbation experiment.”

For example, a researcher could silence a specific target at the genetic level to confirm it as a driver of disease. If a gene knockout or similar perturbation does not yield downstream changes as seen in a multiomics analysis, the researcher can investigate the other identified targets as potential drivers.

Multiomics Applications

Scientists apply multiomics across a variety of research areas, using it to solve real-world problems.

Multiomics in agriculture and aquaculture

Agricultural researchers have used multiomics to understand mechanisms of growth, senescence, yield, and stress responses in plants, identifying targets for crop improvement in a range of valuable species.⁸

In aquaculture, scientists have applied integrated transcriptomic and metabolomic analyses to identify the molecular mechanism underlying carp muscle quality changes when they are farmed in different aquaculture systems.⁹ In a study of farmed salmon treated for infection using chemotherapeutic agents, scientists used multiomics to reveal pathways involved in the physiological adaptation of the fish to treatment-induced oxidative stress.¹⁰

Multiomics in biomedicine

Advances in multiomics technology have caused a revolution in medical science, identifying complex pathogenesis causes, disease diagnostic biomarkers, and therapeutic targets.¹ “[Scientists] often talk about someone's DNA or their genome being their blueprint, but it's the interaction of a person's genome together with their environment that ultimately controls the outcome or the emergence of disease,” said Cursons.

Cancer researchers like Cursons often use a multiomics approach to unravel the regulatory pathways involved in tumorigenesis or the immune response to cancer. For example, researchers have used genome, transcriptome, and proteome data to identify subtypes of kidney cancer.² Multiomics approaches have also been used to identify biomarkers for many cancers that could not be identified using stand-alone omics approaches, and to predict survival in liver cancer.²

By including the microbiome in their multiomics analysis, researchers can elucidate previously unknown disease pathogenesis and treatment resistance mechanisms in cancer, or identify microbiome-based biomarkers that can predict treatment response.¹¹ “There's now a massive amount of evidence that the composition of the microbes in a person's gut can significantly influence how they respond to [immune checkpoint blockade drugs such as] PD-L1 and PD-1 inhibitors, which are increasingly used in cancer immunotherapy,” Cursons explained.

FAQ

What is multiomics?

Multiomics is an integrative analysis technique that examines several omes or omics datasets such as the genome, epigenome, microbiome, transcriptome, proteome, and metabolome.

What are the four most common types of omics research?

Genomics, epigenomics, transcriptomics, and proteomics are often listed first among multiomics categories. There are many additional types of omics that scientists investigate, including metabolomics, lipidomics, metagenomics or microbiomics, and glycomics.

This article was originally published on August 19, 2024. It was updated on February 13th, 2025, by Deanna MacNeil, PhD.

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