rcbio

The genome computes in 3 dimensions.

Bioinformatic frameworks don’t.

The genome has evolved over eons into the intricate 3D structure we see today—from nuclear envelope-tethered LADs, through the complex knots of A-compartments, and to the vivid patterns of TADs and plexi that sculpt our gene networks.

While only ~2% of our genome codes for proteins, the remaining ~98% plays a crucial regulatory role. Depending on cell and tissue type, non-coding regions selectively contact coding promoters—particularly those marked by acetylated histones—to orchestrate gene expression with remarkable precision.

The complexity deepens when we consider population-specific recombination blocks that generate varying chromatin looping structures across diverse human populations. This means that truly understanding disease variants from GWAS and functional genomics studies requires building large cohorts of deeply sequenced chromatin contact maps spanning the breadth of human genetic diversity.

But once you have these rich contact maps, what do you do with them? Which computational frameworks make sense? How do you begin asking the right scientific questions?

That's where I come in!

I develop computational approaches to unlock biological insights from 3D genome architecture, bridging population genomics with chromatin structure to understand how genetic variation shapes gene regulation and disease susceptibility.

Interested in solving the same problem?

Reach out!

rishi@rcbio.nl

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