name: borzoi description: > 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. license: Apache-2.0 origin: openai4s category: biomodels requirements: [gpu] capabilities: network: mode: raw_required domains: [] metadata: # SKILL.md loads johahi/borzoi-replicate-0 — a PyTorch port of Calico's # Borzoi ("ported weights (with permission)"). The HuggingFace model card # for that exact artifact states License: cc-by-4.0. Calico's CODE repo is # Apache-2.0, but the weights the skill downloads carry CC-BY-4.0. The model # card is where the license is declared (info_url — not a ToU page). # verified 2026-06-30 third_party: - kind: weights name: Borzoi (PyTorch port) provider: Calico Life Sciences license: CC-BY-4.0 info_url: https://huggingface.co/johahi/borzoi-replicate-0

Borzoi — DNA → Functional Track Prediction

Prerequisites

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. Read compute_details({provider, mode:'read'}) for an environment with borzoi-pytorch, then:

c = host.compute.create(provider)
job = c.submit_job(
    intent="Borzoi track prediction for 1 locus — 1×GPU, ~2 min",
    inputs=[{"src": "borzoi_run.py", "dst_filename": "borzoi_run.py"}],
    command="python3 borzoi_run.py",   # env selection is host-specific — see compute_details for your provider
    outputs=["tracks.npz"],
    timeout_seconds=1800,
)
print(job.job_id)   # cell ends here — kernel never blocks on compute

Then poll from a later cell. .result() is one non-blocking probe of the remote and is what harvests the outputs once the job is terminal — nothing runs in the background, so a job you never poll is never harvested. While the job is still running it returns {"status": "running", …}; end the cell and call it again later:

r = c.attach_job(job_id).result()   # {status, exit_code, output_files,
                                    #  featured_files, remote_workdir, …}
if r["status"] == "succeeded":
    for path in r["featured_files"]:      # paths under hpc/<job_id>/
        host.save_artifact(path)
    c.close()
# `unknown` is not a finished job — poll again rather than closing over it.

See the remote-compute-ssh / remote-compute-nvidia skill for the orchestration details.

If the provider exposes a weight-cache mount, point HF_HOME at it inside borzoi_run.py (path is in compute_details).

Troubleshooting

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