/borzoi

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
category: biomodels
requirements: [gpu]
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

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

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

Retain the exact returned `job_id`. Query that saved ID with the non-blocking `c.attachJob(job_id).status()` or `.result()` when its state or result is relevant; do not scan Job history. A final `.result()` read reports whether its follow-up was `suppressed` or had already been `committed`; otherwise the app starts the later analysis turn for an unread final result. See the `remote-compute-ssh` skill for details.

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

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) |

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