Functional Genomics and Genome-Wide Technologies
Measurement at scale
Omics strategies
Functional genomics integrates DNA sequencing, RNA sequencing, epigenomic profiling, chromatin conformation assays, proteomics, and perturbation screens. These methods aim to move from association to mechanism by measuring expression, accessibility, binding, and phenotype under controlled perturbations.
Which assay most directly measures genome-wide chromatin accessibility?
ATAC-seq profiles accessible chromatin by transposase insertion.
Correct answer: ATAC-seq
Why are CRISPR screens powerful for functional genomics?
Pooled CRISPR assays provide causal evidence, not just correlation.
Correct answer: They allow systematic perturbation of genes or regulatory elements to link genotype to phenotype at scale.
Single-cell and spatial methods
Heterogeneity and context
Single-cell RNA sequencing, single-cell ATAC-seq, lineage tracing, and spatial transcriptomics reveal cell-state heterogeneity hidden in bulk assays. These approaches are crucial for developmental biology, tumor ecology, and tissue organization, where rare subpopulations can drive phenotype.
Common genomics platforms
| Platform | Measures | Major limitation |
|---|---|---|
| RNA-seq | Transcript abundance | Loses spatial context |
| ATAC-seq | Open chromatin | Does not directly identify causal regulator |
| ChIP-seq | Protein-DNA binding or histone marks | Requires high-quality antibodies |
| Hi-C | 3D genome contacts | Contact frequency is indirect |
What is a major advantage of single-cell over bulk transcriptomics?
Single-cell methods reveal variation among individual cells that bulk averages obscure.
Correct answer: It resolves cell-to-cell heterogeneity
What is the main interpretive challenge of Hi-C data?
Physical proximity does not by itself establish regulatory function.
Correct answer: Contacts are probabilistic and reflect population-averaged proximity, not direct causal interactions.
Data-analysis caution
High-dimensional genomics requires strong controls for batch effects, multiple testing, cell-state composition, and reference bias.