gi-promoter

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.
version	0.1.0
author	ClawBio + Genomic Intelligence
domain	genomics
license	MIT
tags	["genomics","promoter","transcription","regulatory","dna-lm","transformer","gi-api"]
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."}]
dependencies	{"python":">=3.10","packages":["requests>=2.31"]}
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."}]
endpoints	{"cli":"python skills/gi-promoter/gi_promoter.py --input {input_file} --output {output_dir}"}
metadata	{"openclaw":{"requires":{"bins":["python3"],"env":[],"config":[]},"always":false,"emoji":"🧬","homepage":"https://docs.genomicintelligence.ai","os":["darwin","linux"],"install":[{"kind":"pip","package":"requests","bins":[]}],"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"]}}

🧬 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. The skill refuses to run unless GI_API_KEY is set — cp .env.example .env && set -a && source .env && set +a to use the shared ClawBio hackathon key (50 concurrent / 120 rpm), or request an individual key at [e-mail]. Prefer a browser? The same models run interactively at https://genomicintelligence.ai. Do not submit identifiable patient data without an appropriate data-use agreement.

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; the partner key is bundled for the ClawBio hackathon (GI_API_KEY env overrides).
• 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. Authenticate: resolve key — --api-key → GI_API_KEY env → bundled hackathon key (concurrent=50, rpm=120).
3. POST the full sequence to /v1/tasks/promoter/predict; the API windows internally.
4. Render: write report.md (summary + region table), result.json (full {data, meta} envelope), reproducibility/.

CLI Reference

# 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

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 ships with a hackathon partner key. For production / heavier use, request your own key ([e-mail]) and:

export GI_API_KEY=gi_yourkeyhere
python skills/gi-promoter/gi_promoter.py --demo

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.