Borzoi
Predict genome-wide functional tracks (RNA-seq, CAGE, DNase, ChIP) from DNA sequence...
Install
npx skills add aipoch/open-science --skill borzoi -g -a claude-code -y- Repository
- aipoch/open-science5.4k
- Category
- Design
- Updated
- 3d ago
- License
- Apache-2.0
- Context cost
- Lightabout 723 tokens
- Runs scripts
- No, instructions only
What it does
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.
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
