alterlab-borzoi
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.
适合你，如果正在研究非编码区变异对基因表达或染色质的影响。
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~/.claude/skills/(项目级 .claude/skills/)~/.codex/skills/npx oh-my-skill add alterlab-ieu/alterlab-academic-skills/alterlab-borzoicurl -fsSL https://oh-my-skill.com/install.sh | bash -s -- alterlab-ieu/alterlab-academic-skills/alterlab-borzoinpx oh-my-skill verify alterlab-ieu/alterlab-academic-skills/alterlab-borzoi怎么用
技能原文 SKILL.md
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.