Overview
Borzoi (Linder et al. 2025; calico/borzoi) is a sequence-to-function deep-learning
model: given a DNA sequence over a long genomic context, it predicts genome-wide functional
tracks — RNA-seq, CAGE, ATAC-seq, and ChIP coverage across many assays/tissues. Its headline
use is non-coding variant effect scoring: run the reference and alternate alleles through
the model and compare predicted tracks to estimate a regulatory variant's impact on expression
or chromatin.
It predicts function from sequence; it does not look up known variants. For a variant's
population frequency use alterlab-gnomad; for clinical significance use alterlab-clinvar.
When to Use This Skill
Use this skill when the user wants to:
- Predict functional tracks (RNA-seq/CAGE/ATAC/ChIP) from a DNA sequence or locus.
- Score a non-coding / regulatory variant's predicted effect (ref vs. alt).
- Run in-silico mutagenesis to find driver bases in a regulatory element.
- Prioritize candidate regulatory variants by predicted functional impact.
Does NOT Trigger
| Scenario | Use instead |
|---|---|
| Look up a variant's population frequency | alterlab-gnomad |
| Look up a variant's clinical significance | alterlab-clinvar |
| Predict a protein-structure / coding effect | alterlab-alphafold |
| Single-cell foundation-model tasks | alterlab-scgpt |
| Standard variant calling from reads | alterlab-nf-core-sarek (or the relevant pipeline skill) |
Core Capabilities
1. Track prediction from sequence
The quickest path is the PyTorch port, which loads ported weights straight from the Hub:
from borzoi_pytorch import Borzoi
# replicates 0-3, human by default; 'johahi/borzoi-replicate-0-mouse' for mouse heads
model = Borzoi.from_pretrained("johahi/borzoi-replicate-0").eval().cuda()
# one-hot encode the reference window around your locus, then:
# predictions = model(one_hot_batch) # (batch, tracks, bins)
The reference TensorFlow implementation (calico/borzoi on top of calico/baskerville)
is the source of truth for the published results and ships the variant-scoring and
interpretation tutorials. Either way: provide a genome window (coordinates + reference, or a
FASTA), one-hot encode it, and read predicted coverage across the output tracks.
2. Non-coding variant effect scoring
The core workflow: build the reference and alternate sequences for a variant, predict tracks for each, and quantify the difference (e.g. SAD/SED-style scores) to estimate the variant's regulatory effect. Prioritize candidates by the magnitude of predicted change.
3. In-silico mutagenesis
Systematically mutate bases across a regulatory element and read the predicted-track deltas to localize functionally important positions (motif/driver discovery).
4. GPU and dispatch
Borzoi takes long context and is GPU-heavy; genome-wide or many-variant scans should be
dispatched via alterlab-remote-compute (submit → poll → harvest). Flashzoi
(johahi/flashzoi-replicate-0..3, needs FlashAttention-2) is a drop-in ~3x faster variant at
comparable accuracy — worth it for large scans.
Resources
references/borzoi_usage.md— install/pinning, sequence extraction, predict calls, ref/alt variant scoring, in-silico mutagenesis, and Enformer lineage. Loaded on demand.
Part of the AlterLab Academic Skills suite.