--- title: "Multi-omics Basics" lang: ko url: https://www.gereallife.com/ko/services/resources-basics.html canonical: https://www.gereallife.com/ko/services/resources-basics.html entity: Shanghai Gereallife Biotech Co., Ltd updated: 2026-11-02 --- KEY POINTS - 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 ## 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 | Omics layer | Targets | Mainstream technologies | Data per sample | Turnaround | | --- | --- | --- | --- | --- | | Genomics | DNA sequence & variation | WGS / WES / Targeted | 30–90 GB | 3–7 working days | | Transcriptomics | RNA expression abundance | RNA-seq / scRNA-seq | 5–10 GB | 3–5 working days | | Proteomics | protein expression & modification | DIA / TMT / PRM | 10–50 GB (raw data) | 5–10 working days | | Metabolomics | small-molecule metabolites | LC-MS / GC-MS | 1–5 GB | 5–7 working days | | Epigenomics | DNA / histone modification | WGBS / ATAC-seq | 20–60 GB | 5–10 working days | ## Q&AFAQ Can I just run RNA-seq and skip the other omics layers? The transcriptome only reflects gene-expression dynamics. The genome reveals genetic potential, the proteome presents the functional executors and the metabolome is closest to phenotype; cross-validating the layers also improves biomarker-discovery reliability. Which two omics layers go well together if my budget is tight? Common pairings are genome + transcriptome for eQTLs and expression regulation, transcriptome + proteome for post-transcriptional regulation efficiency, and proteome + metabolome for enzyme function and pathway activity; full-omics integration is the most comprehensive strategy. How many proteins can one proteomics run identify? 8,000–12,000 proteins per run; 4D proteomics with ion mobility further improves identification depth and quantification accuracy. How long does the analysis take for each omics layer? According to the comparison table: genomics 3–7 working days, transcriptomics 3–5, proteomics 5–10, metabolomics 5–7 and epigenomics 5–10. What size metabolites can you detect, and how many? Metabolomics targets small-molecule metabolites under 1500 Da and can detect hundreds to thousands, using LC-MS/MS, GC-MS or NMR. Need study design? Free study design and sample assessment, response within 1 hour. Contact the team → Call us： 400-660-8826 Scan to chat on WeChat