Explore the 3D Genome
Architecture of Drug Targets
Gene-Maps decodes the spatial language of the human genome, because where a gene lives in 3D nuclear space is as critical as what it encodes.
Every query integrates live data across five dimensions: evolutionary conservation, chromatin accessibility, protein interaction topology, Hi-C contact frequency, and tissue-resolved expression, all converging into a single spatial pharmacogenomics score.
Identify high-confidence drug targets, assess CRISPR edit safety at base-pair resolution, and visualise how genome architecture shapes therapeutic opportunity.
ENSEMBL · UCSC PhyloP · STRING · GTEx v8 · Hi-C
Spatial Networks
Interactive 3D nucleus scene — gene beads, Hi-C contact arcs and TAD domain hulls
Spatial Score
5-component weighted score: conservation, accessibility, PPI centrality, Hi-C contacts, GTEx expression
Conservation
Real ENSEMBL ortholog data across 10 model organisms with percent identity
CRISPR Safety
Deterministic TAD disruption risk (CTCF density) + PhyloP conservation constraint
Drug Target
Druggability scoring combining spatial features, structural context, and tissue specificity
The Science Behind Gene-Maps
Understanding why 3D genome architecture changes how we find drug targets.
Your DNA is 3D, Not Linear
The 3 billion base pairs of human DNA are folded into the nucleus of every cell — a space just 6 microns across. This folding is not random. DNA is organized into loops, compartments, and Topologically Associating Domains (TADs): ~1 Mb regions where genes and their regulatory enhancers are physically close to each other.
The Hi-C technique maps these 3D contacts genome-wide by cross-linking DNA strands that are spatially close, then sequencing the ligation junctions. Gene-Maps uses pre-computed Hi-C contact frequencies to build the spatial interaction network you see in the Network tab.
TADs and CTCF Insulators
TAD boundaries are anchored by CTCF, a zinc-finger protein that acts as a genomic insulator. CTCF sites are loaded at boundaries to prevent enhancers inside one TAD from activating genes in an adjacent TAD.
The CRISPR Safety tab uses UCSC ENCODE CTCF occupancy data to estimate how many CTCF binding sites are near your proposed edit position. Dense CTCF clustering signals a TAD boundary — editing there carries higher disruption risk.
Why 3D Genome = Better Drug Targets
Traditional druggability screens look at protein structure in isolation. But a gene's position in 3D chromatin space tells you much more: genes at the center of spatial interaction networks (spatial hubs) tend to be master regulators, expressed broadly, and under strong evolutionary constraint — all hallmarks of high-quality drug targets.
Finan et al. (2017, Sci Transl Med) showed that targets with genetic evidence from human disease loci — which cluster in active TADs — have a 2× higher clinical success rate.
How Gene-Maps Calculates Scores
Spatial Score: 0.25 × conservation + 0.20 × accessibility + 0.25 × centrality + 0.20 × Hi-C + 0.10 × expression
Glossary
- TAD
- Topologically Associating Domain — a self-interacting chromatin region (~1 Mb) defined by Hi-C data.
- CTCF
- CCCTC-binding factor — zinc-finger protein that marks TAD boundaries and acts as a chromatin insulator.
- Hi-C
- Genome-wide 3D chromatin conformation capture technique measuring physical proximity of DNA loci.
- PhyloP
- Per-base conservation score from alignment of 100 vertebrate genomes. Positive = conserved.
- Ortholog
- A gene in another species that evolved from the same ancestral gene. Percent identity = amino acid similarity.
- STRING DB
- Database of known and predicted protein-protein interactions, scored by experimental and computational evidence.
- GTEx
- Genotype-Tissue Expression project — gene expression levels across 54 human tissues.
- Druggability
- Likelihood that a protein can be modulated by a small molecule or biologic with therapeutic effect.
ENSEMBL REST API · UCSC PhyloP100way · STRING DB · GTEx v8 · Hi-C (pre-computed)