Omics in cancer research
Under scientific review
This introductory page is under scientific review. Use it for orientation, not clinical decision-making.
Overview of molecular data modalities and how they connect to bioinformatics workflows. For integration methods, see Multi‑omics integration.
What “omics” means here
Omics means measuring many molecular features at once — genomes, transcripts, proteins, metabolites, or epigenetic marks — and analyzing them as a system. In oncology, these layers help characterize tumors, stratify patients, and suggest therapy.
Main modalities
| Modality | What it captures | Typical use in cancer |
|---|---|---|
| Genomics | DNA sequence and structure (SNVs, indels, CNVs, SVs) | Driver mutations, tumor heterogeneity, resistance |
| Epigenomics | DNA methylation, histone marks, chromatin accessibility | Subtypes, plasticity, drug targets |
| Transcriptomics | RNA expression (bulk or single-cell) | Pathways, immune infiltration, signatures |
| Proteomics | Proteins and modifications | Functional state, drug targets, phospho-signaling |
| Metabolomics | Small molecules and metabolic flux | Metabolic vulnerabilities, drug response |
Omics 101 series
Short primers per modality (expandable over time):
From raw data to insight
- Sequencing workflows: From FASTQ to variants
- Single-cell and spatial: Intro to single-cell and spatial
- Biomarkers and tests: Biomarkers and companion diagnostics
Multi-omics and data practice
- Integration strategies (fusion, graphs, joint models): Multi‑omics integration
- Finding data: Data & APIs and Datasets
- Responsible use: Data governance & LGPD
- Modeling pitfalls: ML in oncology — pitfalls
Why it matters for technologists
Omics data are large, noisy, and clinically sensitive. Closing the gap between files in a bucket and reproducible, interpretable results is exactly the kind of “hack” HackCancer is about: tools, standards, and execution — not hype.