alterlab-borzoi

name	alterlab-borzoi
description	Predict genome-wide functional genomics tracks from DNA sequence with Borzoi (Linder 2025) — a sequence-to-function model outputting RNA-seq, CAGE, ATAC, and ChIP coverage across long context, used to score non-coding and regulatory variant effects. Use when predicting functional tracks from a DNA sequence, scoring a non-coding/regulatory variant's effect on expression or chromatin, or doing in-silico mutagenesis of a locus. To LOOK UP a variant's population frequency prefer alterlab-gnomad; for its clinical significance prefer alterlab-clinvar; for protein-structure effects prefer alterlab-alphafold; for single-cell foundation models prefer alterlab-scgpt. Part of the AlterLab Academic Skills suite.
license	Apache-2.0
allowed-tools	Read Write Edit Bash(python:*) Bash(uv:*)
compatibility	Runs Borzoi (`calico/borzoi`; install per repo — TODO(verify) exact pin) under `uv run python`. Model weights download once and cache; a CUDA GPU is recommended (the model takes long DNA context and is heavy on CPU). Inputs are DNA sequences (FASTA / genome coordinates + a reference); outputs are multi-track coverage arrays. Dispatch large scans via alterlab-remote-compute.
metadata	{"skill-author":"AlterLab","version":"1.0.0"}

Borzoi (sequence → function)

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

# calico/borzoi — API sketch; TODO(verify) against installed borzoi
# 1) extract the reference sequence window around a locus
# 2) run the model to get multi-track predicted coverage
# (see references/borzoi_usage.md for the exact model-loading + predict calls)

Provide a genome window (coordinates + reference, or a FASTA); the model returns predicted coverage across its 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).

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.