borzoi
GitHub基于DNA序列预测基因组功能轨迹（如RNA-seq、ChIP等），用于评估变异调控效应、生成覆盖轨迹及优先排序非编码变异。
Trigger Scenarios
Install
npx skills add aipoch/open-science --skill borzoi -g -y
SKILL.md
Frontmatter
{
"name": "borzoi",
"license": "Apache-2.0",
"category": "biomodels",
"metadata": {
"third_party": [
{
"kind": "weights",
"name": "Borzoi (PyTorch port)",
"license": "CC-BY-4.0",
"info_url": "https:\/\/huggingface.co\/johahi\/borzoi-replicate-0",
"provider": "Calico Life Sciences"
}
]
},
"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.\n",
"requirements": [
"gpu"
]
}
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) |
Next: combine track deltas with evo2 likelihood deltas for a
two-axis variant prioritisation.
Version History
- 44394f0 Current 2026-09-11 11:09
