structural variant
Definition
Structural variants (SVs) are large-scale genomic alterations typically exceeding 50 base pairs that include deletions, duplications, insertions, inversions, and translocations. Unlike single nucleotide variants (SNVs), SVs can affect entire genes, regulatory regions, or chromosomal segments, potentially disrupting multiple functional elements simultaneously. They account for significant genetic diversity between individuals and play crucial roles in evolution, disease susceptibility, and cancer development. SVs are particularly challenging to detect using standard sequencing approaches due to their size and complexity, often requiring specialized bioinformatics tools and long-read sequencing technologies. Understanding SVs is essential for comprehensive genomic analysis, as they can alter gene dosage, create fusion genes, or disrupt regulatory landscapes, leading to profound phenotypic consequences.
Visualize structural variant in Nodes Bio
Researchers can map structural variants onto gene regulatory networks to visualize their cascading effects on downstream pathways. By connecting SVs to affected genes, transcription factors, and their targets, Nodes Bio enables identification of disrupted regulatory circuits and compensatory mechanisms. Network analysis reveals how deletions or duplications impact pathway connectivity, helping prioritize variants with the greatest functional impact on disease-relevant biological processes.
Visualization Ideas:
- Network showing genes disrupted by deletions/duplications connected to affected biological pathways
- Fusion gene products and their novel protein-protein interaction networks in cancer
- Regulatory network disruption maps showing transcription factors affected by inversions or translocations
Example Use Case
A cancer genomics team investigating treatment resistance in glioblastoma discovers recurrent chromosomal deletions affecting the CDKN2A/B locus across patient samples. Using network analysis, they map how this structural variant disrupts cell cycle regulation and connects to amplification of EGFR through compensatory pathways. By visualizing the network of genes affected by multiple co-occurring SVs, they identify synthetic lethal targets and predict which patients might respond to CDK4/6 inhibitors based on their specific SV profiles and downstream pathway alterations.