---
name: gi-promoter
description: Detect promoter regions in DNA sequences using the Genomic Intelligence G0 transformer (GENA-LM BERT Large), via the hosted /v1/tasks/promoter/predict API. Returns per-window promoter probabilities
  and called regions.
license: MIT
metadata:
  openclaw:
    requires:
      bins:
      - python3
      env: null
      config: null
    always: false
    emoji: 🧬
    homepage: https://docs.genomicintelligence.ai
    os:
    - darwin
    - linux
    install:
    - kind: pip
      package: requests
      bins: null
    trigger_keywords:
    - promoter
    - promoter prediction
    - predict promoter
    - find promoter
    - promoter region
    - TSS prediction
    - transcription start site
    - gi promoter
    - genomic intelligence promoter
    - G0 promoter
    - GENA-LM promoter
  author: ClawBio + Genomic Intelligence
  demo_data:
  - path: example_data/promoter_tp53.fa
    description: TP53 locus, gene-sense (chr17:7661779-7687546, GRCh38, 25.8 kbp; TP53 is minus-strand, so this is the reverse complement) — bundled real reference sequence.
  dependencies:
    python: '>=3.10'
    packages:
    - requests>=2.31
  domain: genomics
  endpoints:
    cli: python skills/gi-promoter/gi_promoter.py --input {input_file} --output {output_dir}
  inputs:
  - name: input_file
    type: file
    format:
    - fa
    - fasta
    - fna
    description: Single-record FASTA. Any length; the API windows automatically (default model uses 2000 bp context, 1000 bp stride).
    required: false
  outputs:
  - name: report
    type: file
    format: md
    description: Markdown report — sequence + model metadata, called promoter regions, headline counts.
  - name: result
    type: file
    format: json
    description: Full `{data, meta}` response from the GI API plus a flattened summary.
  - name: reproducibility
    type: directory
    description: command.sh + environment.json for exact-rerun reproducibility.
  tags:
  - genomics
  - promoter
  - transcription
  - regulatory
  - dna-lm
  - transformer
  - gi-api
  version: 0.1.0
---

# 🧬 gi-promoter

You are **gi-promoter**, a ClawBio agent that calls the **Genomic Intelligence** promoter-prediction model. Given a DNA sequence (any length), it returns per-window promoter probabilities and called regions, all in a few hundred milliseconds via the hosted API.

> ⚠️ **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](#authentication) below.

## Trigger

**Fire this skill when the user says any of:**
- "predict promoters in this sequence"
- "find promoters in [gene/region]"
- "is this a promoter?"
- "score this for promoter activity"
- "gi-promoter", "G0 promoter", "GENA-LM promoter"
- "transcription start site prediction", "TSS prediction"

**Do NOT fire when:**
- The user asks for splice sites → `gi-splice`
- The user asks for enhancer activity → `gi-enhancer`
- The user asks for chromatin state → `gi-chromatin`
- The user asks for gene/transcript structure → `gi-annotation`

## Why This Exists

- **Without it**: A user with a multi-kbp sequence has to spin up a GPU, download the GENA-LM weights, tokenize, window, and run inference themselves.
- **With it**: One CLI call → annotated report in <1 s for typical sequences. The model is hosted; see [Authentication](#authentication) for key setup.
- **Why ClawBio**: Hosted G0 inference plus ClawBio's reproducibility bundle and orchestration chaining (`gi-promoter` → `gi-expression` → `variant-annotation`).

## API Backed

`POST https://api.genomicintelligence.ai/v1/tasks/promoter/predict` — default model `g0-promoter-2000bp` (GENA-LM BERT Large, 2000 bp context, 1000 bp prediction window). Override with `--model g0-promoter-large-300bp` (faster) or `--model dnabert-promoter-2000bp` (DNABERT, 6-mer tokenization).

## Workflow

1. **Parse**: read single-record FASTA via the shared `clawbio.gi.gi_client.read_fasta` helper (uppercase, strip non-ACGTN).
2. **POST** the full sequence to `/v1/tasks/promoter/predict`; the API windows internally.
3. **Render**: write `report.md` (summary + region table), `result.json` (full `{data, meta}` envelope), `reproducibility/`.

## CLI Reference

```bash
# Demo — bundled TP53 region
python skills/gi-promoter/gi_promoter.py --demo --output /tmp/gi-promoter-demo

# Your own FASTA
python skills/gi-promoter/gi_promoter.py --input my_region.fa --output report_dir

# Faster 300-bp model
python skills/gi-promoter/gi_promoter.py --demo --model g0-promoter-large-300bp

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

## Demo

```bash
python clawbio.py run gi-promoter --demo
```

Bundled fixture is the TP53 locus (19 kbp, GRCh38). Expect ~20 windows, near-zero promoter calls at the default 0.5 threshold (TP53 promoter sits in a small region, not most of the locus) — proves the model is discriminating.

## 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
```

## Gotchas

- **Don't pre-window the sequence yourself.** Submit the full region — the API stride/window. Pre-windowing inflates rate-limit usage and gives identical results.
- **Strand matters — submit gene-sense.** The promoter model is strand-sensitive (trained on EPDnew 5'→3' coding-strand sequence). For minus-strand genes, reverse-complement to gene-sense before submission; the plus (genomic) strand returns near-zero (e.g. TP53 on the plus strand finds **0** promoters, on the coding strand finds its real promoters). The bundled TP53 fixture is already gene-sense.
- **The hackathon key is shared.** If you hit `429`, you're sharing 50 concurrent / 120 rpm with everyone else. Set `GI_API_KEY` to your own key for serious work.
- **N-content**: long stretches of `N` produce low-confidence calls; pre-trim if the region is mostly gap.

## Output Structure

```
output_dir/
├── report.md              # Headline counts, region table, model + timing
├── result.json            # Full {data, meta} envelope from the API
└── reproducibility/
    ├── command.sh         # Exact invocation
    └── environment.json   # API base, model, request_id, timestamp
```

## Integration with Bio Orchestrator

Routes here on: "promoter", "TSS prediction", "find promoter", "score promoter activity".

Chains with: `variant-annotation` (annotate variants overlapping called promoters), `gi-expression` (predict expression for sequences scored as promoters), `gwas-lookup` (look up variants in called promoter regions).

## Safety

Research tool. Not a clinical assay. Hosted inference — the sequence you submit traverses the GI API endpoint. Do not submit identifiable patient data without an appropriate agreement.
