Multiomics is transforming biological research by integrating genomic, transcriptomic, epigenomic, proteomic, and microbiome data to create a more complete understanding of biological systems.
What is multiomics?
Multiomics is a biological analysis approach that combines multiple “-omes” to study life. There are many pillars of the multiome, including: genome, transcriptome, epigenome, microbiome (which can include the metagenome and metatranscriptome), proteome, metabolome, and exposome.
Why study multiomics?
Studying more than one “-ome” is important because the different layers of -omics interact and influence one another.
For example, DNA mutations in the genome may cause changes in methylation patterns or chromatin structure in the epigenome that affects the transcribed RNA in the transcriptome, which in turn affects protein synthesis of the proteome. Altogether, these processes contribute to health and disease. By integrating different omics, one can have a deeper and systematic understanding of the cause and effect of disease, and ways to detect, prevent, and treat it.
What can HiFi do for multiomics?
HiFi sequencing is uniquely positioned to study multiomics. HiFi can sequence long, native DNA sequences – capturing the genome and methylome (which is a part of the epigenome) immediately. HiFi can also sequence full-length RNA transcripts, which are the precursor for proteins. Finally, the microbiome, consisting of bacteria, archaea, and viruses that live within organisms and in the environment, also are composed of DNA or RNA genomes that can be sequenced with HiFi.
E-book
Deep dive into the HiFi multiome
What is the genome, transcriptome, and epigenome? Why choose HiFi for studying these omics? How does multiomics apply to human disease research, therapeutics development, agriculture, and more? Find out in this comprehensive e-book.
The ‘omics of HiFi
Genomics
Study the DNA blueprint of an organism, from small mutations and large structural variations to chromosomal rearrangements.
Whole genome sequencing Targeted sequencing
Transcriptomics
Characterize RNA transcripts in any species, healthy or diseased, to find the functional link to protein changes.
Epigenomics
Detect changes beyond the DNA, from methylation to chromatic accessibility, to understand drivers in disease and development.
Metagenomics
Profile complex microbial communities that live inside organisms, in soil, oceans, and more.
Why long reads for multiomics?
Many multiomics studies aim to understand how genomic variation influences gene expression, cellular function, and disease biology. Long-read sequencing enables researchers to analyze structural variants, DNA methylation, haplotypes, and full-length transcripts within the same biological system, helping reveal how genetic changes drive downstream molecular phenotypes.
For example, researchers can link structural variants to altered transcript isoforms in cancer and rare disease samples, providing insights that may be missed with fragmented short-read sequencing data.
Long read for Genomics: Resolve the Full Spectrum of Genetic Variation
Genomics provides the foundation for many multiomics workflows, but important forms of genetic variation can be difficult to characterize with conventional sequencing technologies. Due to its long read lengths and high accuracy, long-read sequencing improves detection of structural variants, repeat expansions, complex rearrangements, and variation within repetitive genomic regions.
PacBio whole genome sequencing enables highly accurate variant discovery and more complete genome assemblies. These capabilities help researchers connect genomic variation to downstream molecular phenotypes across human disease, population genomics, and precision medicine.
Long reads for Epigenomics: Understand the Regulatory Layer Beyond the Genome
Epigenetic modifications regulate gene expression and cellular identity. PacBio HiFi sequencing can detect DNA methylation while generating highly accurate long-read sequence data, allowing researchers to incorporate epigenetic information into multiomics studies without separate bisulfite sequencing workflows.
This capability supports studies of gene regulation, developmental biology, imprinting disorders, and cancer epigenetics while simplifying experimental design.
Long reads for Transcriptomics: Capture Full-Length RNA Molecules
Transcriptomic data are central to many multiomics studies, yet gene-level measurements often miss important biological complexity. The PacBio Iso-Seq method enables direct sequencing of full-length transcripts, allowing researchers to identify alternative splicing events, novel isoforms, cancer fusion transcripts, and allele-specific expression.
This isoform-level resolution helps researchers understand how genomic and epigenomic variation influence gene expression, disease mechanisms, and therapeutic response.
Single-Cell and Spatial Multiomics: Add Cellular and Tissue Context
Single-cell and spatial multiomics extend transcriptomic analysis by revealing where genes and transcript isoforms are expressed across individual cells and tissues. Unlike bulk RNA sequencing, these approaches provide the cellular and spatial context needed to study complex tissues and heterogeneous cell populations.
Long-read sequencing enables isoform-level analysis in single cells and spatially resolved samples, helping researchers identify cell-type-specific transcript isoforms, characterize transcriptional diversity, and uncover regulatory mechanisms that may be obscured in bulk measurements.
These capabilities are particularly valuable in cancer research, neuroscience, immunology, and developmental biology, where tissue architecture and cellular heterogeneity are critical for biological discovery.
Long reads for metagenomics: Understand microbial communities with greater resolution
Microbial communities contain thousands of organisms that interact across complex ecosystems, from the environment to within living organisms. Long-read sequencing improves metagenomic studies by enabling more complete microbial genome assemblies, strain-level resolution, and accurate characterization of mobile genetic elements.
These advantages help researchers link genes, pathways, and functional traits to specific organisms within a community. Applications include microbiome research, environmental monitoring, antimicrobial resistance studies, and industrial biotechnology.
Multiomics data integration: Connecting biological layers
Multiomics studies generate genomic, transcriptomic, epigenomic, proteomic, and microbiome data that must be analyzed together to uncover biological insights. Advances in bioinformatics, machine learning, and AI are enabling researchers to integrate these diverse datasets and identify relationships that may be missed when studying a single omics layer alone.
Long-read sequencing contributes to multiomics data integration by providing highly accurate genomic, transcriptomic, and epigenomic measurements that can be connected across biological layers.
Long-Read Multiomics in the AI Age
AI-driven multiomics is increasingly being applied to biomarker discovery, drug discovery, precision medicine, and therapeutic development. However, AI models depend on the quality and completeness of the underlying data. Long-read multiomics generates rich, high-resolution datasets for AI by capturing full-length transcripts, structural variants, haplotypes, and epigenetic modifications within the same biological context.
As researchers apply AI to predict disease mechanisms, identify biomarkers, and model cellular behavior, comprehensive multiomics datasets become increasingly important. Long-read sequencing reduces ambiguity introduced by fragmented short-read data, enabling more accurate representation of transcript isoforms, complex genomic regions, and regulatory variation.
These capabilities are especially valuable for precision medicine, drug discovery, cancer genomics, and single-cell/spatial multiomics, where AI models must integrate multiple layers of biological information to identify meaningful patterns.
Frequently asked questions about multiomics
Multiomics combines multiple layers of biological information—including genomics, transcriptomics, epigenomics, proteomics, and metabolomics—to provide a more comprehensive understanding of biological systems than any single omics approach alone.
Long-read sequencing such as PacBio HiFi sequencing enables researchers to capture full-length transcripts, detect structural variants, characterize DNA methylation, and resolve complex genomic regions, providing richer data for multiomics analysis and integration.
Single-cell multiomics measures multiple molecular features from individual cells, enabling researchers to study cellular heterogeneity, cell states, and regulatory mechanisms that may be obscured in bulk samples.
Spatial multiomics combines molecular profiling with spatial information, allowing researchers to understand where genes, transcripts, proteins, or other molecular features are located within tissues.
PacBio HiFi sequencing can be used characterize transcript isoforms either at the single-cell or spatial level.
AI and machine learning help researchers integrate genomic, transcriptomic, epigenomic, and other multiomics datasets to identify patterns, predict biological outcomes, and accelerate biomarker and therapeutic discovery.
Common types of multiomics include genomics, transcriptomics, epigenomics, proteomics, metabolomics, microbiomics, and combinations such as single-cell and spatial multiomics.
Long-read sequencing such as PacBio HiFi sequencing enables researchers to capture full-length transcripts, detect structural variants, characterize DNA methylation, and resolve complex genomic regions, providing richer data for multiomics analysis and integration.
Single-cell multiomics measures multiple molecular features from individual cells, enabling researchers to study cellular heterogeneity, cell states, and regulatory mechanisms that may be obscured in bulk samples.
Spatial multiomics combines molecular profiling with spatial information, allowing researchers to understand where genes, transcripts, proteins, or other molecular features are located within tissues.
PacBio HiFi sequencing can be used to characterize transcript isoforms either at the single-cell or spatial level.
AI and machine learning help researchers integrate genomic, transcriptomic, epigenomic, and other multiomic datasets to identify patterns, predict biological outcomes, and accelerate biomarker and therapeutic discovery.
Multiomics in action: Applications from genome to function
Blog
Rare disease research: Multiomics approaches for identifying causal variants
While whole genome sequencing (WGS) is usually first choice of molecular method in identifying variants that could explain a disease phenotype, there is increasing utility in adding the epigenome and transcriptome to rare disease studies to get the full picture from sequence to function. For example, researchers combined a Fiber-seq treated WGS library with Kinnex RNA library on the same sequencing run to explain the molecular basis of a rare disease patient from the Undiagnosed Diseases Network (UDN) (watch the webinar here).
Neuroscience research: Multiomics reveals complex gene regulation in the brain
Many genes associated with neurodevelopment and neurodegenerative diseases are known to have complex splicing patterns, meaning one gene could generate many difference transcript isoforms that translate to different proteins. A multiomic study combined long-read RNA sequencing with proteomics to reinterpret genetic risk for autism spectrum disorder (ASD) (watch the webinar here).
Meanwhile, due to the highly specialized functions of different brain regions, researchers have shown the benefits of using single-cell transcriptome sequencing to study neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, and Dementia with Lewy Body.
Finally, promising research has emerged from researchers at the University of College London studying Parkinson’s disease combining bulk RNA sequencing with proteomics data to design potential therapeutics.
Publication
Cancer research: Multiomics for precision oncology
Cancer is driven by genomic, transcriptomic, and epigenomic alterations, making it an ideal application for multiomics approaches. Researchers have used long-read genome and transcriptome sequencing to connect complex structural variants and oncogene amplifications with expressed fusion transcripts in breast cancer, revealing how genomic rearrangements shape cancer biology.
In ovarian and prostate tumors, PacBio HiFi sequencing has also been used to integrate genome-wide methylation and chromatin accessibility measurements, enabling the identification of tumor-derived DNA molecules and uncovering cancer-specific epigenetic regulation. Meanwhile, others have used single-cell RNA sequencing to track treatment responses and clonal evolution in leukemia. There is also emerging evidence that alternative splicing may play a role in certain types of lung cancer.
Also, emerging liquid biopsy approaches combine genomic, copy number, mutation, and fragmentomics signals from circulating DNA to improve early cancer detection and molecular stratification. Together, these studies demonstrate how integrating multiple molecular layers can provide a more complete understanding of cancer initiation, progression, and treatment response.
Spotlight
Plant and animal research: Multiomics for agriculture, biodiversity, and conservation
There is immense benefit to understanding the genetics of all life on earth, from microbes to plants to marine life – and help with understanding biodiversity, track conservation efforts, and improve agriculture. The Vertebrate Genome Project, for example, aims to sequence all 70,000+ extent vertebrate species, and has utilized HiFi whole genome sequencing with RNA sequencing to create high-quality reference genomes and annotations. Similarly, the Tree of Life project has sequenced over 3000 genomes and transcriptomes with a final goal of 70,000 eukaryotic organisms (animals, plants, fungi, and protists) across Britain and Ireland.
Spotlight
Metagenomics and microbiome research: Connecting microbes to health and the environment
The human body contains approximately 38 trillion microbial cells and 30 trillion human cells. Therefore, a complete picture of human biology often requires studying both the human genome and the microbial communities that coexist with it. Researchers have used HiFi to sequence the gut microbiome of under-nourished children in a Malawian cohort to predict health trajectories based on metagenomics data (watch the webinar here).
The vast amount of novel microbial species with yet to be discovered genes also present a rich source of input for machine learning methods. AI-driven biotech companies are using PacBio long read metagenomic data to train large language models that could be used for drug discovery and biological design.
Start your multiomics journey today
Now that you know the value of multiomics for scientific research, stay up to date by checking out upcoming events, finding a local sequencing provider, exploring our datasets, or contact a PacBio scientist to get started with sequencing today!