base pair
Definition
A base pair is the fundamental unit of DNA structure, consisting of two complementary nucleotide bases connected by hydrogen bonds. In DNA, adenine (A) pairs with thymine (T) via two hydrogen bonds, while guanine (G) pairs with cytosine (C) via three hydrogen bonds. This complementary base pairing, known as Watson-Crick pairing, enables DNA's double helix structure and is essential for genetic information storage, replication, and transmission. The human genome contains approximately 3 billion base pairs. Base pair sequences determine gene structure, regulatory elements, and ultimately protein coding information. Variations in base pairs, such as single nucleotide polymorphisms (SNPs), can influence disease susceptibility, drug response, and phenotypic traits, making base pair analysis fundamental to genomics research.
Visualize base pair in Nodes Bio
Researchers can visualize base pair variations and their downstream effects using network graphs in Nodes Bio. Map SNPs to affected genes, protein interactions, and phenotypic outcomes. Create networks showing how base pair mutations propagate through regulatory pathways, connecting genomic variants to disease mechanisms. Visualize relationships between specific base pair positions, transcription factor binding sites, and gene expression changes across different conditions or patient populations.
Visualization Ideas:
- SNP-to-phenotype networks showing base pair variants connected to disease outcomes
- Gene regulatory networks mapping base pair sequences in promoter regions to transcription factor binding
- Comparative genomics networks displaying base pair conservation across species
Example Use Case
A pharmacogenomics research team investigates why certain patients experience adverse reactions to warfarin. They identify a SNP involving a single base pair change (C to T) in the CYP2C9 gene at position rs1799853. Using network analysis, they map how this base pair variation affects enzyme activity, warfarin metabolism rates, and bleeding risk. The visualization reveals connections between the variant, reduced protein function, altered drug clearance pathways, and clinical outcomes, enabling development of personalized dosing guidelines based on patient genotypes.