Common types of omics data and their biological significance.
| Omics layer | Primary data type | Biological significance | Key characteristics | Common platforms |
|---|---|---|---|---|
| Genomics | DNA sequences, variants (SNPs: single nucleotide polymorphisms, CNVs: copy number variations, mutations) | Germline and somatic alterations; disease susceptibility; drug metabolism; heritable traits | Static across cell types; high dimensionality (millions of variants); discrete, sparse data | Whole-genome sequencing, whole-exome sequencing, SNP arrays |
| Transcriptomics | RNA abundance (mRNA, ncRNA) | Gene expression patterns; regulatory networks; cellular state; functional activity | Dynamic, cell-type specific; count-based, overdispersed; high dimensionality (thousands of transcripts) | RNA-seq, microarrays |
| Proteomics | Protein abundance, post-translational modifications | Functional effectors; signaling pathways; drug targets; direct phenotype mediators | Moderate dimensionality; continuous intensity data; partial correlation with transcriptomics | Mass spectrometry, antibody-based assays |
| Metabolomics | Metabolite concentrations (small molecules) | Metabolic state; physiological readout; closest to phenotype; biomarker-rich | Lower dimensionality (hundreds to thousands); high chemical diversity; dynamic range | NMR, mass spectrometry |
| Epigenomics | DNA methylation, histone modifications, chromatin accessibility | Gene regulation; environmental response; cell identity; tissue specificity | Tissue-specific; spatial dependencies; binary or continuous signals | Bisulfite sequencing, ChIP-seq, ATAC-seq |
| Microbiomics | Microbial taxonomic and functional profiles | Host-microbe interactions; immune modulation; drug metabolism; disease associations | Compositional data; high sparsity; cross-sectional or longitudinal | 16S rRNA sequencing, metagenomic sequencing |
During the preparation of this work, the authors used Google AI tools to improve language and readability. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.
MH: Conceptualization, Writing—original draft, Visualization. SH: Writing—review & editing, Supervision. KA: Methodology, Writing—review & editing, Formal analysis. MW: Methodology, Formal analysis, Writing—review & editing. All authors have read and approved the final version of the manuscript.
The authors declare that they have no conflicts of interest.
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No new data were generated or analyzed in this review. All referenced datasets are publicly available from the sources cited.
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