Multi-omics Basics

01What Is Multi-Omics?

Multi-omics is the core strategy of systems biology: by integrating data across molecular layers—genome, transcriptome, proteome, metabolome and epigenome—it reconstructs the complete regulatory network from gene to phenotype. Compared with any single omics, multi-omics resolves biological complexity from multiple dimensions at once: the genome reveals genetic potential, the transcriptome reflects gene-expression dynamics, the proteome presents the functional executors, and the metabolome is the end-product layer closest to phenotype. Through cross-validation and integrative analysis, multi-omics not only improves the reliability of biomarker discovery but also reveals how genotype shapes phenotype through multi-layered regulation, providing comprehensive molecular evidence for precision medicine, disease-mechanism research and drug development.

Core Strengths of Multi-Omics

• More comprehensive: covers the full information flow from DNA → RNA → protein → metabolite
• More precise: cross-validation improves biomarker-discovery reliability
• Deeper mechanism: reveals how genotype shapes phenotype through multi-layer regulation
• Personalized: delivers multi-dimensional molecular portraits for precision medicine

Common Multi-Omics Combinations

• Genome + Transcriptome: resolving eQTLs and expression regulation
• Transcriptome + Proteome: assessing post-transcriptional regulation efficiency
• Proteome + Metabolome: resolving enzyme function and pathway activity
• Full-omics integration: the most comprehensive systems-biology strategy

02The Omics Layers in Detail

Genomics

Studies the complete DNA sequence of an organism and its variation. Core technologies include whole-genome sequencing (WGS), whole-exome sequencing (WES) and targeted sequencing. Detects SNPs, InDels, CNVs, SVs and other variant types—the foremost layer for understanding the genetic basis of life.

• Key technologies: Illumina NGS、PacBio HiFi、Oxford Nanopore
• Data output: 30–90 GB per sample (WGS 30×)
• Analysis: variant calling, annotation, pathogenicity classification

Transcriptomics

Quantifies the types and abundance of all RNA transcripts in cells or tissues, including mRNA, lncRNA and miRNA. Single-cell RNA-seq and spatial transcriptomics further deliver cellular resolution and spatial localization.

• Key technologies: bulk RNA-seq, scRNA-seq, spatial transcriptomics
• Data output: 5–10 GB per sample
• Analysis: differential expression, pathway enrichment, cell clustering

Proteomics

Large-scale identification and quantification of protein expression, modification and interaction. Mass spectrometry (DDA/DIA/PRM) is the core technology; 4D proteomics (ion mobility) further improves identification depth and quantification accuracy.

• Key technologies: DIA、TMT、PRM、4D-Proteomics
• Identification depth: 8,000–12,000 proteins per run
• Analysis: differential proteins, PTMs, protein-interaction networks

Metabolomics

Profiles the complete set of small-molecule metabolites (MW <1500 Da) in a biological system. As the end products of gene and protein function, metabolites directly reflect physiological and pathological states—making metabolomics the layer "closest to phenotype."

• Key technologies: LC-MS/MS、GC-MS、NMR
• Coverage: hundreds to thousands of metabolites detected
• Analysis: metabolite identification, pathway analysis, disease biomarkers

Epigenomics

Studies heritable gene-expression regulation that does not alter the DNA sequence, including DNA methylation, histone modification and chromatin accessibility—an important bridge between environment and genes.

• Key technologies: WGBS、RRBS、ChIP-seq、ATAC-seq
• Analysis: methylation levels, open-chromatin regions, TF binding sites
• Featured applications: early development, tumorigenesis, environmental-exposure studies

03The General Multi-Omics Workflow

From scientific question to biological discovery, multi-omics research follows a rigorous standardized workflow

Question

define the biological question, objectives and hypotheses

Study Design

sample-size estimation, group design, batch-effect control

Sampling

standardized collection, aliquoting, freezing and QC records

Instrument Run

data acquisition on sequencers / mass spectrometers, raw-data output

Bioinformatics

QC, alignment/database search, quantification, differential and enrichment analysis

Integration

cross-omics association, pathway integration, biological conclusions

04Technology Comparison Across Omics Layers