--- name: gi-annotation description: 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: 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: - 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 . **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:** - "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 ```bash # 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 ` 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: ```bash # Repo root (git clone) — or ~/.claude/plugins/cache/clawbio/clawbio// 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-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.