hybrid capture
Definition
Hybrid capture is a targeted sequencing enrichment method that uses biotinylated oligonucleotide probes to selectively capture and isolate specific genomic regions of interest from a DNA or RNA library. The probes hybridize to complementary target sequences through Watson-Crick base pairing, and the probe-target complexes are then pulled down using streptavidin-coated magnetic beads. This technique enables deep sequencing of selected genes, exomes, or custom genomic regions while reducing sequencing costs and increasing coverage depth compared to whole-genome sequencing. Hybrid capture is widely used in clinical diagnostics, cancer genomics, and population studies where focused analysis of specific genomic loci is required, offering superior sensitivity for detecting rare variants and structural alterations in targeted regions.
Visualize hybrid capture in Nodes Bio
Researchers can visualize hybrid capture experimental designs as networks connecting probe sets to target genes, then link captured variants to affected pathways and phenotypes. Network graphs can map relationships between captured genomic regions, their associated genes, protein interactions, and downstream biological processes, enabling comprehensive interpretation of targeted sequencing results and identification of functional connections between detected variants.
Visualization Ideas:
- Probe-to-gene target mapping networks showing coverage and enrichment efficiency
- Variant-to-pathway networks connecting captured mutations to affected biological processes
- Patient similarity networks based on shared variants in captured genomic regions
Example Use Case
A cancer genomics team designs a hybrid capture panel targeting 500 known oncogenes and tumor suppressors across 200 patient samples. After sequencing, they identify recurrent mutations in DNA repair genes and receptor tyrosine kinases. Using network visualization, they map how captured variants cluster within specific signaling pathways, reveal co-occurring mutations suggesting synthetic lethality relationships, and identify patient subgroups with distinct mutational signatures that may respond differently to targeted therapies, ultimately informing personalized treatment strategies.