Genomic Intelligence

Genomic Intelligence — DNA Sequence Models

Genomic Intelligence (GI) serves transformer DNA language models over six sequence-analysis tasks on managed GPUs. Give it a gene symbol, a genomic region, or a DNA/FASTA sequence; it returns structured predictions — promoter regions, splice sites, enhancer activity, chromatin state, expression (log TPM), and de-novo gene annotation. Nothing runs locally: no model weights, no GPU, no heavy Python stack. It is a thin client over a hosted, versioned inference API.

Official docs: docs.genomicintelligence.ai · REST contract at api.genomicintelligence.ai/v1/openapi.json · hosted MCP server at https://mcp.genomicintelligence.ai/mcp

When to use this skill

Use GI when the user has DNA and wants a model prediction:

• Find promoters in a genomic region (promoter)
• Predict splice donor/acceptor sites (splice)
• Score enhancer activity — developmental & housekeeping (enhancer)
• Annotate chromatin state across hundreds of tracks (chromatin)
• Predict expression as log(TPM+1) from a sequence + cell-type context (expression)
• Annotate genes/transcripts de novo, no reference needed (annotation)
• Find the genes in a region and predict each one's expression (composite)

Not for local alignment, variant calling, or file I/O — use a local tool (BioPython, bcftools) for those. GI is for model inference from sequence.

Research and development use. Not for clinical or diagnostic decisions.

Two ways to call GI

Hosted MCP server (keyless; preferred on MCP hosts)

GI hosts an MCP server at https://mcp.genomicintelligence.ai/mcp (Streamable HTTP). When your agent host supports MCP, prefer it: it works keyless against a rate- and concurrency-limited public demo tier, and an optional gi_ bearer key raises those limits. It exposes acquisition tools that return a sequence handle (sequence_ref) and predict_* tools that take that handle, so large sequences stay out of the context. See MCP workflow below and references/mcp.md.

REST API (universal)

Plain HTTP with requests against https://api.genomicintelligence.ai/v1. The REST path requires a GI_API_KEY (a gi_ bearer). Use it on any host, in scripts, or when you need the raw envelope. See Core REST workflow.

Access and authentication

1. The hosted MCP demo is keyless — try it with nothing set.
2. The REST `/v1` API needs a key, sent as Authorization: Bearer <key>. Request one at contact@genomicintelligence.ai.
3. Never hardcode the key. Read it from the GI_API_KEY environment variable (or a .env via python-dotenv). Never commit keys.

export GI_API_KEY="gi_yourkeyhere"     # optional for MCP; required for REST
export GI_BASE_URL="https://api.genomicintelligence.ai"   # override for staging

Keys are scoped to a partner tier with concurrency and per-minute caps. A 429 means you hit a cap — back off and retry, or ask GI to raise your tier.

The six tasks

Each task is its own published operation with its own request schema, its own minimum length, and its own closed options object — POST /v1/tasks/promoter/predict, /v1/tasks/splice/predict, /v1/tasks/enhancer/predict, /v1/tasks/chromatin/predict, /v1/tasks/annotation/predict, /v1/tasks/expression/predict. Each path is a literal string, so nothing needs to be constructed, and there is no shared PredictRequest schema. Body is {sequence, sequence_name?, model?, options?}, returning a {data, meta} envelope. What differs per task:

Recommended mode is guidance, not a constraint — every task accepts both. Omit Prefer for a synchronous 200; send Prefer: respond-async for a 202 plus GET /v1/tasks/jobs/{job_id}. The one enforced limit is per operation: where /v1/openapi.json publishes x-sync-limit-bp on a POST, a synchronous request above that length is 413 sync_too_large — 200,000 bp on annotation and 50,000 bp on the composite workflow as of info.version 2026.09.10.1. Read the field rather than memorising the numbers; the other predict tasks carry no limit today.

The minimum is admission control, not regime. A request above the floor but shorter than the selected model's bio_spec.context_window_bp is accepted and scored — against a window padded out to the context window. Enhancer is the sharp case: the floor is 50 bp but the context window is 249 bp, so 50–248 bp is scored mostly on padding. Compare your length against context_window_bp from GET /v1/tasks/{task}/models to know whether the model saw real sequence. Longer-than-context input is fine — the scanner steps a prediction window at a time and pads only the final partial window.

Under the floor and over the 500,000 bp cap are both `422 validation_failed` at loc ["body","sequence"]; over-length is not a 413. All lengths are measured after whitespace is stripped, so a line-wrapped FASTA body can be pasted verbatim (a > header line still fails the alphabet check).

options is typed and closed (additionalProperties: false) per task — an unknown key is a hard 422 validation_failed with type: "extra_forbidden", never ignored:

Prefer: respond-async is a declared header on all six predict operations and on the composite, not just annotation — see Async.

Omit `model` and the API uses the task's default — that is the recommended call. Default model IDs are intentionally not documented here: defaults change and retired IDs fail hard, so never hardcode one. To pin a model, or to pick a non-human one (Drosophila, yeast, and Arabidopsis models exist for several tasks), discover IDs at call time with GET /v1/tasks/{task}/models (REST) or list_models (MCP) — and never invent one. Full per-task output shapes are in references/tasks.md.

expression is the strictest of the six: alone among them its schema requires options as well as sequence. Three hard rules it enforces — every violation is a 422, nothing is padded or clamped, and there is no opt-out flag, header, or query parameter:

• It always scores exactly one 9,198 bp TSS-centred window — sequence[tss_index-4599 : tss_index+4599]. The endpoint itself accepts 9,198–500,000 bp; anything below 9,198 bp is rejected outright.
• `tss_index` is required unless the sequence is exactly 9,198 bp. It is the 0-based TSS offset into the whitespace-stripped sequence, bounded by