# Genome Therapy

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Qihao Duan](https://cerebralvalley.ai/u/Qihao)
- **GitHub:** https://github.com/Qihao-Duan/PerturbExpression.git
- **Demo video:** https://youtu.be/2IDTpwpvsTU
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/278

Sequence-to-expression "oracles" (Enformer, Borzoi, AlphaGenome, Evo2) and the generative-DNA designers built on them all aim to predict how cis-regulatory DNA sets a gene's expression. But they are rarely scored on the quantity that matters for regulatory therapeutics: the change in expression (Δ) a real perturbation produces, measured against experimental ground truth and against a trivial baseline.

CIS-Δ is a reproducible benchmark that does exactly that. It scores a model's predicted Δ for a (cell-context, cis-intervention) pair against experimental truth — single-cell CRISPRi/CRISPRa, eQTL fine-mapping, reporter MPRA — across seven scoring axes, anchored on 90,955 K562 CRISPRi pairs. We ship a one-file submission format, a scoring harness with gene-clustered bootstrap CIs and DeLong tests, an auto-generated leaderboard, and a self-test that round-trips committed truth to one part in a million.

The finding is a carefully-bounded negative result. On distal-enhancer CRISPRi, a naive distance-to-TSS baseline out-ranks Borzoi and Enformer (AUROC 0.87), because 71–88% of real regulatory links fall outside the models' receptive field. On natural-variation eQTLs the oracles do clear the distance prior (AlphaGenome, significant after FDR correction) — but statistical fine-mapping still out-ranks them. And cell-type specificity can't be demonstrated at all: the eQTL ground truth is 93–96% shared across tissues, so that axis is under-powered by construction. Even the 2026 state of the art does not close the gap.

Why it matters: before trusting a sequence model to design a regulatory edit, you need to know it can score one better than distance-to-TSS. CIS-Δ is the instrument that checks — and a living leaderboard the field can submit to.

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