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. REST prediction and job operations need a key, sent as Authorization: Bearer <key>. Public GET /v1/tasks/{task}/models discovery needs no key and is rate-limited by source IP; inspect model windows and bounds before requesting access. See the current authentication contract. Request a prediction key 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:

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