Omics in cancer research
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.