Borzoi

Skill xuzhougeng/wisp-science/skills/borzoi

Open-source, local-first desktop AI research workbench for scientific computing with Python/R, MCP bioinformatics tools, SSH/WSL/GPU runtimes, and OpenAI/Anthropic models.

Install

npx -y skills add xuzhougeng/wisp-science --skill borzoi

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What its author says it does

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Predict genome-wide functional tracks (RNA-seq, CAGE, DNase, ChIP) from DNA sequence with Borzoi. Use this skill when: (1) Scoring the regulatory effect of a variant on expression/accessibility, (2) Generating predicted coverage tracks for a locus, (3) Prioritising non-coding variants by predicted track delta.

The file declares its own license as Apache-2.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

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Borzoi — DNA → Functional Track Prediction

Prerequisites

Requirement	Minimum	Recommended
Python	3.10+	3.11
CUDA	12.1+	12.4+
GPU VRAM	16 GB	24 GB+

How to run

from borzoi_pytorch import Borzoi

model = Borzoi.from_pretrained("johahi/borzoi-replicate-0").cuda().eval()
# input: (batch, 4, 524288) one-hot DNA  → output: (batch, tracks, 6144) bins

Borzoi consumes ~524 kb one-hot windows and emits binned predictions across 7,611 human tracks (the separate 2,608-track mouse head is off by default; enable via enable_mouse_head=True and select with forward(..., is_human=False)). For variant scoring, run ref/alt windows centred on the variant and compare per-track output.

Output format

(B, T, L) tensor — T tracks × L 32-bp bins. Track metadata (assay, biosample) is in borzoi_pytorch.pytorch_borzoi_model.TRACKS_DF (or model.tracks_df when using the AnnotatedBorzoi subclass) — the base Borzoi model has no targets attribute.

Remote compute

Needs ≥24 GB VRAM and either pre-cached HF weights or egress to huggingface.co. Use a selected and probed ssh:<alias> context and load remote-compute-ssh. Confirm borzoi-pytorch and the cache location, then submit a self-contained runner with run_in_context:

{
  "context_id": "ssh:gpu-box",
  "title": "Borzoi prediction for one locus",
  "command": "source ~/miniforge3/etc/profile.d/conda.sh && conda activate borzoi && HF_HOME=/srv/model-cache python borzoi_run.py --output /home/me/wisp-results/borzoi/tracks.npz",
  "timeout_secs": 1800,
  "input_paths": ["runs/borzoi_run.py"],
  "output_specs": [
    {
      "glob": "ssh://gpu-box/home/me/wisp-results/borzoi/tracks.npz",
      "kind": "npz",
      "residency": "remote"
    }
  ]
}

Replace context, environment, cache, and output paths with discovered values. Call monitor_run once to wait, get_run once for a snapshot, or cancel_run to stop.

Troubleshooting

Symptom	Cause	Fix
`module has no __version__`	Package exposes no attr	Use `importlib.metadata.version("borzoi-pytorch")`
Shape mismatch on input	Wrong window length	Pad/crop to 524288 bp (fixed; not exposed as a model attribute)

Next: combine track deltas with evo2 likelihood deltas for a two-axis variant prioritisation.