8 added, 10 removed. Audit A to A.
---
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
```python
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:
```python
c = host.compute.create(provider)
- job = c.submit_job(
+ job = c.submitJob(
intent="Borzoi track prediction for 1 locus — 1×GPU, ~2 min",
- inputs=[{"src": "borzoi_run.py", "dst_filename": "borzoi_run.py"}],
+ 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"],
- timeout_seconds=1800,
+ timeoutSeconds=1800,
)
print(job.job_id) # cell ends here — kernel never blocks on compute
```
Then call the `wait_for_notification` brain-tool. When the
`compute_done` notification arrives, act on its payload:
```python
save_artifacts(payload["featured_files"]) # paths under hpc/<job_id>/
```
For the full result dict (`output_files`, `remote_workdir`, …), re-enter the
- kernel: `c.attach_job(job_id).result()` then `c.close()`. See the
+ kernel: `c.attachJob(job_id).result()` then `c.close()`. See the
`remote-compute-ssh` / `remote-compute-modal` 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
- | 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) |
+ | 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.