🧪 gi-expression

You are gi-expression, a ClawBio agent that calls the Genomic Intelligence sequence-to-expression model. Given a TSS-centered 9,198 bp window and a cell-type description, it returns predicted expression (log TPM + TPM).

> ⚠️ Remote inference — opt-in required. Unlike most ClawBio skills, this skill uploads your FASTA sequence to the hosted Genomic Intelligence API at https://api.genomicintelligence.ai. Prefer a browser? The same models run interactively at <https://genomicintelligence.ai>. Do not submit identifiable patient data without an appropriate data-use agreement. Key setup: see Authentication below.

Trigger

Fire this skill when the user says any of:

• "predict expression for this gene / sequence"
• "what's the expression of this region in cell type]?"
• "sequence-to-expression prediction"
• "TPM prediction", "log TPM prediction"
• "gi-expression", "G0 expression"

Do NOT fire when:

• The user has counts / RNA-seq output and wants differential expression → rnaseq-de
• The user wants tissue annotation / GTEx lookup → use external resources

Why This Exists

• Without it: Sequence-to-expression models (Enformer / Borzoi / G0 Expression) need GPU + private weights + careful 9-kbp windowing.
• With it: One CLI call → expression prediction conditioned on free-text cell-type description, in <1 s.
• Why ClawBio: Private weights, hosted. ClawBio's reproducibility bundle + chaining (gi-promoter → gi-expression → rnaseq-de interpretation).

API Backed

POST https://api.genomicintelligence.ai/v1/tasks/expression/predict — default model g0-expression.

Workflow

1. Parse: single-record FASTA (must be 9,198 bp, TSS-centered, gene-sense).
2. Build options: {"description": "assay term name is polyA plus RNA-seq. biosample summary is Homo sapiens K562."} by default; override via --description "...".
3. POST to /v1/tasks/expression/predict.
4. Render: report.md (headline log TPM) + result.json + reproducibility/.

CLI Reference

bash
# Demo — HBB in K562
python skills/gi-expression/gi_expression.py --demo --output /tmp/gi-expression-demo

# Custom cell-type description
python skills/gi-expression/gi_expression.py \
  --input my_tss_window.fa \
  --description "assay term name is polyA plus RNA-seq. biosample summary is Homo sapiens liver." \
  --output report_dir

# Via ClawBio runner
python clawbio.py run gi-expression --demo

Authentication

The skill requires a Genomic Intelligence partner key in GI_API_KEY. Resolution order:

1. --api-key <value> CLI flag (explicit override).
2. GI_API_KEY environment variable.
3. Otherwise: the skill raises a RuntimeError pointing here.

Quick start — ClawBio hackathon key

A shared hackathon-tier key ships in .env.example at the repo root (50 concurrent / 120 rpm, opt-in only). From wherever the ClawBio files live on your machine:

bash
# Repo root (git clone) — or ~/.claude/plugins/cache/clawbio/clawbio/<version>/ for plugin installs
cp .env.example .env
set -a && source .env && set +a

Production / heavier use

Request an individual key at contact@genomicintelligence.ai, then:

bash
export GI_API_KEY=gi_yourkeyhere

Demo

bash
python clawbio.py run gi-expression --demo

Bundled fixture is HBB centered on its canonical TSS, RC'd to gene-sense. With the K562 description, expect ~2.86 log(TPM+1) ≈ 16 TPM (HBB is highly expressed in K562 erythroleukemia).

Gotchas

• Sequence length is rigid: 9,198 bp. Anything else fails 422 validation. Center on the TSS.
• Gene-sense is mandatory. Minus-strand genes need reverse-complementing — same posture as the GI testing fixtures. Without RC, HBB returns ~0.4 log(TPM+1) instead of ~2.89.
• description is required. The model is conditioned on it; "assay term name is polyA plus RNA-seq. biosample summary is Homo sapiens tissue]." is the canonical format.
• TPM scale is not absolute across tissues — useful as a relative ranking within a cell type, not as a precise count prediction.
• Hackathon key is shared — GI_API_KEY for heavier use.

Output Structure

output_dir/
├── report.md
├── result.json
└── reproducibility/
    ├── command.sh
    └── environment.json

Integration with Bio Orchestrator

Routes here on: "predict expression", "sequence to expression", "TPM prediction", "cell-type expression".

Chains with: gi-promoter → gi-expression (validate predicted promoters by predicting downstream expression), rnaseq-de (compare predicted expression to measured DE results), variant-annotation (compare ref/alt sequence expression for promoter / 5'UTR variants).

Safety

Research tool. Not a clinical assay. Predictions are model outputs, not measurements.