# Ash0723

- **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:** [Auroni Deep](https://cerebralvalley.ai/u/ASH0723)
- **GitHub:** https://github.com/auronideep777/RegScope
- **Demo video:** https://youtu.be/TuI5c0yHcIc
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/17

RegScope- Now available at https://auronideep777.github.io/RegScope/
https://github.com/auronideep777/RegScope

The short version:
RegScope is a small suite of browser-based tools that do one connected thing: read a piece of DNA, predict how active it is as a regulatory element, and then work out what a single-base change would do to it, not just at the level of "which transcription-factor motif did you break," but across the structural language of the genome as well: G-quadruplexes, i-Motifs, Z-DNA, R-loops, cruciforms, triplex/H-DNA, DNA shape, duplex stability, and nucleosome positioning.
It grew out of the Gladstone / Built-with-Claude research question, train a model that reads DNA to predict regulatory activity, then ask what a single base does, using the Deng et al. developing-cortex MPRA dataset (Science, adh0559). The result is four linked pieces plus a landing page, all of which run entirely in a web browser with nothing to install.

Why it needed to exist:
If you sit at a bench and you have a non-coding variant you care about, the honest state of the art is fragmentation. You go to one web server to ask about G-quadruplexes, a different one for R-loops, a third for Z-DNA and repeats, a fourth for DNA shape, a fifth for duplex melting, a sixth for nucleosomes, each with its own input format, its own output, and no shared frame of reference. Separately, if you want a data-driven prediction of regulatory activity you reach for a heavyweight deep-learning model (Enformer, Sei, Basenji-style) that needs a GPU, a genome build, and real engineering effort, and that tells you almost nothing about why a base matters structurally.
Nobody had put the two halves together in a way a non-specialist could actually use. RegulatoryScope's argument is simple: a regulatory variant's effect is a sum of grammar the field usually studies in isolation, so the tool should let you see all of it at once, the trained activity model, the structural decomposition, and the per-mutation breakdown, reproducibly, transparently, and without a cluster.

How it works, four tools, one pipeline:
A Home page ties the suite together. From there:
●	The MPRA Dashboard. A sequence-to-function deep-learning model trained on the cortex MPRA: a reverse-complement-equivariant, residual-dilated convolutional tower with squeeze-excite and attention pooling, run as a 3-seed ensemble. It predicts regulatory activity from raw DNA, flags silencers, scores single-base variant effects, runs saturation mutagenesis, and interprets learned motifs against JASPAR via TOMTOM. Every number is reported on held-out chromosomes.
●	The structural feature engine (11 families). For any window of DNA it scores G-quadruplex (G4Hunter), i-motif, DNA shape (MGW/Roll/ProT/HelT/EP), duplex destabilization (nearest-neighbour ΔG / SIDD), Z-DNA, R-loop-forming potential, cruciform (inverted repeats), triplex/H-DNA (mirror repeats), nucleosome positioning, plus transcription-factor motifs. Each is a fast, transparent model with a documented path to its gold-standard tool.
●	The decomposition & rigor layer (Additional Data). Six per-family dashboards plus a combined atlas that ask, with real statistics, how much each structural axis adds to explaining variant effect on top of GC content and TF motifs, nested R², partial correlation controlling for GC, a GC-matched control, and a bootstrap confidence interval. It reports negatives as loudly as positives.
●	SDM Studio. A site-directed-mutagenesis explorer: enumerate every single-base change (or paste a specific list), get an exportable table of all mutations, and click any one to see its predicted impact across all families plus TF motifs, with the reference context and a citation for every method used.
●	RegScope scanner. A base-resolution view of a whole sequence, structure tracks, an in-silico mutagenesis map, CpG islands, ORFs and TF binding, for orienting yourself before you drill into a single variant.
The through-line is that all five share the same structural engine and the same honest reporting style, so a number in the scanner, the SDM table, and the decomposition atlas all mean the same thing.

How I tested it:
Two kinds of claim need two kinds of test, and it matters not to blur them.
Biological accuracy of the structural engine was measured with positive/negative controls: for each discrete family I embedded a canonical motif (a known G4, a C-rich i-motif, a CG/CA Z-former, a G-rich R-loop initiator, an inverted repeat, a homopurine mirror repeat) into random background, generated matched motif-free negatives, and computed the area under the ROC curve (AUROC) of the raw score separating them (n = 60 positives / 60 negatives each). I spot-checked G4Hunter against five textbook G-quadruplexes, and validated the duplex-stability model exactly against published SantaLucia nearest-neighbour values.
Predictive accuracy of the model is quoted directly from the dashboard's held-out-chromosome results, these are the project's own reported numbers, and I present them as reported rather than re-deriving them, while noting how they compare to the baselines shipped alongside.
Software reliability was tested by loading all six pages headlessly and driving the interactive tools through edge cases, empty input, invalid characters, a too-short sequence, an oversized sequence hitting the safety cap, a custom mutation list mixing four notations and deliberate errors, sorting, filtering, family toggling, and every export path, while watching for a single uncaught JavaScript error.
On the testing itself. Every control set was generated programmatically, with canonical motifs implanted at random positions inside random-composition backgrounds, and the AUROC was computed as the rank-based probability that a positive outscores a negative. The full suite was re-run to confirm the scores are deterministic, and the reliability pass deliberately drove each tool through its failure modes as well as its normal path, treating any single uncaught JavaScript error as a failure.
Honest limitations
●	The structural engine ships with fast proxy models. G4/i-motif/R-loop/repeat detection is excellent on controls; DNA shape and nucleosome are directionally right but not validated against DNAshapeR/NuPoP. Every panel says which gold-standard tool to swap in.
●	The decomposition dashboards' extended-family numbers were computed on a synthetic demo cohort to exercise the pipeline end-to-end; they are badged as such and become real the moment the pipeline is pointed at your own MPRA/caQTL table. The RegNetDNA metrics and provenance, by contrast, are real and held-out.
●	Variant-effect discrimination (AUROC ~0.62) is modest, but that is largely the biology: only about 4% of the library's variants have a significant effect, so there is little signal to find, and the honest move is the confidence-weighted ensemble rather than an inflated score.
●	This is a research and hypothesis-generation instrument, not a clinical classifier.
Bottom line
RegulatoryScope does something the field hasn't packaged before: it reads DNA, predicts regulatory activity with a model that is honest about its held-out performance, and then explains a single-base change through the full structural grammar of the genome, with statistics, citations, and exports, entirely in a browser. The predictive model is strong for its task (Spearman ~0.60, AUROC ~0.80), the structural engine is excellent on controls (AUROC 0.96 mean; thermodynamics exact), and the software is reliable (zero errors across every scenario tested). Its honesty about what its features don't do is, in a field full of overclaiming, one of its most useful features.

Datasets used:
Developing human cortex MPRA (training, validation, variant library, and all held-out metrics). Deng C et al., Science 2024, both the primary tissue and organoid arms. The activity table used here is byte-identical to Supplement S1 and the variant library to Supplement S2; splits are by chromosome so held-out chromosomes never appear in training.
JASPAR 2022 core vertebrate motif profiles, used for the transcription-factor scans and for TOMTOM motif matching.
Non-B DB predicted non-B DNA motif tracks, the reference resource the cruciform/triplex and Z-DNA proxies defer to.
WTC11 iPSC data, used only as an independent multi-cell negative-control transfer, deliberately a clean negative.
Structural-engine control sequences (canonical G-quadruplexes such as c-MYC Pu27, VEGF, BCL2, KIT, and the human telomere repeat) are drawn from the standard G-quadruplex literature; the nearest-neighbour thermodynamic parameters are from SantaLucia 1998.

The complete writeup has been uploaded on Github

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