DeepVariant, in your browser via tensorflow.js

This project runs Google's DeepVariant v1.8 WGS model end-to-end in your browser. The candidate engine, the seven-channel pileup encoder, and the backbone InceptionV3 network itself are all here, in TypeScript + tfjs. No server.

I quantized the released checkpoint down to uint8 to shrink it to ~22 MB, ran parity tests against upstream, and the predictions match within rounding noise. Inference runs on your device through tfjs's WebGL backend.

Why does this exist?

Over the last year or so I have been exploring where deep learning meets biology, and DeepVariant felt like the cleanest thing to start from: a CNN doing real, useful work, used in real clinical pipelines, with a paper trail you can sit down and read. While digging into how variant-calling gets reframed as image classification, I stumbled into the official blog post Looking Through DeepVariant's Eyes and got the urge to build a live version of it.

I wanted to take it apart and make it easily traceable, instead of just calling .predict() on a black box. So I rebuilt the encoder and wrapped a visualization layer around it. That turned into this sandbox.

The data within the sandbox is generated synthetically. Each time you click Randomize I regenerate a ~1500 bp reference, drop in a handful of scenarios (het / hom_alt SNVs, small indels, low-quality cases), and simulate reads that cover them, so the model is real, the data feeding it is fabricated, and the placed scenarios act as ground truth.

Some years ago I built a similar in-browser sandbox called artificialneural.network — a toy neural net you can configure layer by layer, compile against a sample dataset, train step by step, and click any neuron to inspect its weights. That one was a way to visualize and teach how an MLP actually moves while it learns. This is what happens when you point the same instinct: interactive, in-browser, p5 + tfjs, every step legible, at a real production model.