gi-annotation

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. Async-only — the pipeline takes ~20 s for ~20 kbp.
version	0.1.0
author	ClawBio + Genomic Intelligence
domain	genomics
license	MIT
tags	["genomics","annotation","gene-prediction","transcript-prediction","gene-structure","dna-lm","gi-api"]
inputs	[{"name":"input_file","type":"file","format":["fa","fasta","fna"],"description":"Single-record FASTA (genomic region; can be tens to hundreds of kbp).","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."}]
dependencies	{"python":">=3.10","packages":["requests>=2.31"]}
demo_data	[{"path":"example_data/annotation_tp53.fa","description":"TP53 locus (chr17:7668402-7687550, GRCh38, 19 kbp) — bundled real reference sequence."}]
endpoints	{"cli":"python skills/gi-annotation/gi_annotation.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":["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"]}}

📜 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 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:

• "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 — annotation is async-only. The pipeline streams progress through GET /v1/tasks/jobs/{job_id} (typically: load → gene-boundaries → gene-intervals → transcripts).

Workflow

1. Parse: single-record FASTA.
2. Authenticate: --api-key → GI_API_KEY → hackathon fallback.
3. Submit async: POST /v1/tasks/annotation/predict with Prefer: respond-async → 202 + job_id.
4. Poll: stream progress (percent, message) until terminal.
5. 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

Demo

python clawbio.py run gi-annotation --demo

Bundled fixture is the TP53 locus (19 kbp). Expect ~5 transcripts (TP53 has multiple annotated isoforms) and a ~20 s wall time.

Gotchas

• Async-only. Don't expect a sync response. The runner handles polling automatically.
• Long input is normal. The model handles tens-to-hundreds of kbp; longer regions take proportionally more time.
• 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 tool. Not a clinical assay. Predicted gene structures are model outputs, not curated reference annotations — for clinical interpretation, anchor to RefSeq / Ensembl.