gi-annotation

name: gi-annotationdescription: Predict gene and transcript structure (intervals, exons, strand) from a DNA sequence using the Genomic Intelligence DNA Annotation model, via the hosted /v1/tasks/annotation/predict API. Submitted asynchronously  — the pipeline takes ~20 s for ~20 kbp.license: MITmetadata:   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:     - gene annotation    - transcript annotation    - annotate sequence    - gene structure prediction    - predict transcripts    - de novo gene prediction    - DNA annotation    - gene boundaries    - exon prediction    - gi annotation    - genomic intelligence annotation  author: ClawBio + Genomic Intelligence  demo_data:   - path: example_data/annotation_tp53.fa    description: TP53 locus (chr17:7668402-7687550, GRCh38, 19 kbp) — bundled real reference sequence.  dependencies:     python: '>=3.10'    packages:     - requests>=2.31  domain: genomics  endpoints:     cli: python skills/gi-annotation/gi_annotation.py --input {input_file} --output {output_dir}  inputs:   - name: input_file    type: file    format:     - fa    - fasta    - fna    description: Single-record FASTA (genomic region), 1,000–500,000 bp (whitespace stripped) — tens to hundreds of kbp is the normal case.    required: false  outputs:   - name: report    type: file    format: md    description: Markdown report — predicted transcripts with start / end / strand.  - name: result    type: file    format: json    description: Full `{data, meta}` response with per-transcript structure.  - name: reproducibility    type: directory    description: command.sh + environment.json.  tags:   - genomics  - annotation  - gene-prediction  - transcript-prediction  - gene-structure  - dna-lm  - gi-api  version: 0.1.0

📜 gi-annotation

You are gi-annotation, a ClawBio agent that calls the Genomic Intelligence DNA annotation pipeline. Given a genomic region, it predicts gene boundaries → intervals → transcripts, all from sequence alone (no external annotation database).

⚠️ 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 same models also 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:

• "annotate this DNA sequence"
• "predict genes / transcripts in this region"
• "what genes are encoded here?" (from sequence, not coordinates)
• "de novo gene prediction"
• "gi-annotation"

Do NOT fire when:

• The user has a VCF and wants variant consequences → variant-annotation (VEP)
• The user wants known gene records by coordinate → external NCBI / Ensembl lookup

Why This Exists

• Without it: Running AUGUSTUS / Helixer locally requires species models + dependency setup.
• With it: One CLI call → predicted transcript structures, in ~20 s for ~20 kbp.
• Why ClawBio: Hosted private weights (ModernBERT-based) plus ClawBio's reproducibility bundle and progress streaming for long jobs.

API Backed

POST https://api.genomicintelligence.ai/v1/tasks/annotation/predict with Prefer: respond-async. The API accepts either delivery mode on every task; this skill always submits async because the pipeline is long-running. The pipeline streams progress through GET /v1/tasks/jobs/{job_id} (typically: load → gene-boundaries → gene-intervals → transcripts).

Contract note. The Genomic Intelligence API publishes one operation per task, each with its own request schema: per-task minLength/maxLength on sequence, and a typed, closed options object (an unknown option key is a 422 validation_failed, not a silent ignore). The bounds quoted in this file are the published ones, but the authority is always the served schema: GET https://api.genomicintelligence.ai/v1/openapi.json.

Workflow

1. Parse: single-record FASTA.
2. Submit async: POST /v1/tasks/annotation/predict with Prefer: respond-async → 202 + job_id.
3. Poll: stream progress (percent, message) until terminal.
4. Render: report.md (transcripts table) + result.json (full response) + reproducibility/.

CLI Reference

# Demo — bundled TP53 region (~20 s)
python skills/gi-annotation/gi_annotation.py --demo --output /tmp/gi-annotation-demo

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

# Via ClawBio runner
python clawbio.py run gi-annotation --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 (opt-in only). Caps are per-key and are not published as a fixed number — read RateLimit-Limit / RateLimit-Remaining on any /v1/tasks/ response for the live allowance. The runner keeps them for you: they are in result.json under rate_limit, and a 429 names them on the error line. From wherever the ClawBio files live on your machine:

# 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:

export GI_API_KEY=gi_yourkeyhere

Demo

python clawbio.py run gi-annotation --demo

Bundled fixture is the TP53 locus (19 kbp). Expect several transcripts — TP53 has multiple annotated isoforms — and a wall time of roughly 20 s.

Gotchas

• Always submitted async. This skill sends Prefer: respond-async and polls, so it never returns a synchronous response — though the API itself serves annotation synchronously when the header is omitted. Delivery mode is a per-request choice, not a property of the task.
• Length bounds are 1,000–500,000 bp, published as minLength / maxLength on AnnotationPredictRequest and counted after whitespace is stripped. Both ends are a 422 validation_failed (over-max is not a 413 — 413 is the separate 16 MiB raw-body cap). The skill rejects either locally before spending a request. The 1,000 bp floor is the highest of the six tasks: the gene finder needs a region, not a single exon.
• Long input is normal. The model handles tens-to-hundreds of kbp up to the 500 kbp cap; longer regions take proportionally more time. Annotation reports bio_spec.context_window_bp: null — there is no sliding-window regime caveat here, unlike promoter / splice / enhancer / chromatin.
• First-call cold-start. The annotation pipeline is the heaviest GI model — first request after a cold service takes ~30+ s; subsequent calls are warm.
• The model is trained on human + a few other vertebrates. Bacterial / fungal / plant predictions are out of distribution.
• Hackathon key is shared. Async jobs count toward concurrent caps too — under heavy hackathon load, you may queue.

Output Structure

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

Integration with Bio Orchestrator

Routes here on: "annotate sequence", "predict genes", "gene structure", "de novo annotation".

Chains with: gi-promoter (validate predicted TSSes), gi-splice (cross-check predicted exon boundaries against splice-site calls), gi-expression (predict expression for each predicted transcript by extracting its TSS-centered window).

Safety

Research and development use. Not for clinical or diagnostic decisions. Predicted gene structures are model outputs, not curated reference annotations — for clinical interpretation, anchor to RefSeq / Ensembl.