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Saperne di piùHome | Analisi omiche | Multi Omics
Multi-omics integrates multiple layers of biological information—typically genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics—to capture a systems-level view of biology. Rather than looking at a single layer in isolation, multi-omics reveals how molecular processes interact to drive phenotype, health, and disease.
Advances in high-throughput assays and bioinformatics make multi-omics possible: robust experimental design, rigorous QC, feature quantification, statistical integration, network modelling, and interpretable visualisation are essential to turn complex data into decisions.
How DNA variation, epigenetic state, RNA, proteins, metabolites, and microbes influence one another.
How biological processes vary across time, space, and cell types.
How diet, drugs, lifestyle, and exposures reshape molecular networks.
Which regulatory and metabolic modules are shared across species or contexts.
Multi-omics research integrates data from multiple molecular layers to understand how complex biological systems function as a whole. Each analytical workflow addresses a distinct question — from identifying layer-specific signals to uncovering the mechanisms that connect them.
Each omic captures a different dimension of biology — from genetic variants and epigenetic marks to RNA transcripts, proteins, metabolites, and microbiota. Integrating these profiles provides a complete view of molecular activity within cells, tissues, and organisms.
How can we quantitatively describe the molecular state in a comparable and integrable way?
Harmonised molecular profiles that enable downstream cross-omic comparisons.
By examining how features from different omics correlate, researchers can discover molecular cascades and regulatory dependencies. These correlations reveal how biological signals propagate across molecular layers.
How do molecular events in one layer influence or predict changes in another?
Multi-layer correlation networks highlighting regulatory relationships and key molecular hubs.
Beyond single-molecule relationships, multi-omics integration maps how coordinated molecular changes affect biological functions and pathways. This involves mapping genes, proteins, and metabolites to shared pathways and quantifying their joint activity.
Which biological processes or pathways are jointly modulated across multiple omic layers?
Functionally coherent pathway-level insights that link molecular variation to phenotype.
This type of analysis looks for hidden patterns shared across different omic layers. By using mathematical models (such as MOFA or DIABLO), it reduces the complexity of large datasets and identifies common trends — for example, molecular signatures linked to a disease, a treatment, or a specific cell type.
Which shared molecular patterns explain the main differences we see across omic layers?
A set of combined molecular signatures that describe how the system behaves as a whole.
Integrated multi-omic data can power predictive models that anticipate clinical outcomes, drug response, or environmental adaptation. Machine learning and AI methods use these high-dimensional features to build interpretable and generalisable predictors.
Can integrated molecular profiles predict biological or clinical outcomes with actionable accuracy?
Robust predictive models and multi-omic biomarkers ready for validation and application.
Transform raw omics data into meaningful, actionable insights through advanced bioinformatics analysis.
Multiomics integration is the key to moving from a single layer to a comprehensive view, enabling us to understand the full complexity of biological systems across different fields.
This approach combines multiple molecular layers from microbial communities (and often their host) — such as genomes, transcripts (metatranscriptome), proteins (metaproteome), and metabolites (metabolome) — to understand how a microbial community functions and interacts.
Here the focus is on combining a person’s genetic variation (germline or somatic) with epigenetic marks, RNA expression, protein levels, metabolites, and even microbial signatures to understand disease and tailor treatment.
In agriculture and environmental science, multi-omics links plant/animal genomes with their epigenome, transcriptome, proteome, metabolome — and often the surrounding soil or microbiome — to improve productivity, resilience and sustainability.
Multi-omics accelerates drug discovery and development by providing mechanistic evidence across molecular layers.
Focus on breakthroughs, not bottlenecks. Harness Sequentia’s cloud-based omics solutions and streamline complex omics workflows into insights that drive innovation and real-world impact.
Connect with our bioinformatics specialists to explore how our services and solutions can support your goals.
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