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.

For research and development use, not clinical or diagnostic decisions.

Two ways to call GI

Hosted MCP server (best for AI agents — keyless)

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 capped public demo quota (zero setup), and an optional gi_ bearer key raises the quota. It exposes acquisition tools that return a sequence handle (sequence_ref) and predict_* tools that take that handle — so large sequences never bloat the context. See [MCP workflow](#mcp-workflow-handle-based) 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](#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](mailto: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

All REST tasks share one shape: POST /v1/tasks/{task}/predict with body {sequence, sequence_name, model?, options?}, returning a {data, meta} envelope. What differs per task:

| Task | Mode | Length bound | Notes | |---|---|---|---| | promoter | sync | 1–500,000 bp | sliding-window promoter regions | | splice | sync | 1–500,000 bp | donor/acceptor sites (long-context BigBird) | | enhancer | sync | 1–500,000 bp | dev + housekeeping scores (DeepSTARR, Drosophila) | | chromatin | sync | 1–500,000 bp | hundreds of tracks (DeepSEA) | | expression | sync | exactly 9,198 bp | log(TPM+1); needs a cell-type description | | annotation | async | 1–500,000 bp | de-novo transcripts; submit + poll |

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.

Two hard rules the model enforces:

• expression needs exactly 9,198 bp, a window centred on the TSS (4,599 upstream + TSS + 4,598 downstream). Any other length is rejected. Use the acquisition helpers below to build it — do not truncate by hand.
• expression needs a description — a cell-type / assay string (e.g. "K562 cells"), passed as options.description.

Sequence acquisition

You rarely start from a raw 9,198 bp string. Acquire sequence first:

• From a gene symbol → MCP fetch_ensembl_sequence(gene=...); **from coordinates** → fetch_region(region=...). Both fetch public Ensembl reference sequence (no key). REST users can query Ensembl REST directly. (find_genes is the annotation task, not an acquisition tool.)
• For expression → use the TSS-centred fetch so the window is exactly 9,198 bp. MCP: fetch_gene_for_expression (handles the centring). Do not build the window by hand.
• From a local FASTA → MCP store_inline_sequence, or read the file yourself for REST. (load_local_fasta exists only in local deployments, not on the hosted server.)
• A demo sequence → MCP load_demo_sequence(name=...) returns a ready handle (great for a keyless smoke test); name is required.

See references/sequence-acquisition.md for the exact Ensembl calls and the expression-window math.

Core REST workflow

Sync tasks (promoter, splice, enhancer, chromatin, expression) are one call:

import os, requests

BASE = os.environ.get("GI_BASE_URL", "https://api.genomicintelligence.ai")
HEADERS = {"Authorization": f"Bearer {os.environ['GI_API_KEY']}"}

def predict(task, sequence, sequence_name, model=None, options=None):
    body = {"sequence": sequence, "sequence_name": sequence_name}
    if model:   body["model"] = model
    if options: body["options"] = options
    r = requests.post(f"{BASE}/v1/tasks/{task}/predict", headers=HEADERS, json=body)
    r.raise_for_status()          # 400 invalid; 401 no/bad key; 413 too long; 429 rate limit
    return r.json()               # {"data": {...}, "meta": {...}}

# Promoter:
out = predict("promoter", seq, "TP53_region")
print(out["data"]["summary"])

# Expression — exactly 9,198 bp + a cell-type description:
out = predict("expression", tss_window_9198bp, "HBB",
              options={"description": "K562 cells"})
print(out["data"]["prediction"]["expression_log_tpm"])

Async: annotation

annotation is submit-then-poll. Send Prefer: respond-async, get a job_id, poll until terminal:

import time

r = requests.post(f"{BASE}/v1/tasks/annotation/predict",
                  headers={**HEADERS, "Prefer": "respond-async"},
                  json={"sequence": seq, "sequence_name": "TP53"})
r.raise_for_status()              # 202 Accepted
job_id = r.json()["data"]["job_id"]

while True:
    j = requests.get(f"{BASE}/v1/tasks/jobs/{job_id}", headers=HEADERS)
    if j.status_code == 200:      # terminal: body is the final {data, meta}
        break
    j.raise_for_status()          # 202 = still running (2xx, won't raise)
    time.sleep(5)                 # ~20 s typical for ~20 kb
transcripts = j.json()["data"]["transcripts"]

MCP workflow (handle-based)

On an MCP host, acquire a handle, then predict against it — sequences stay out of the context:

# 1. Acquire a sequence handle (each returns a sequence_ref):
load_demo_sequence(name="promoter_tp53")  # keyless smoke test; `name` is REQUIRED
fetch_ensembl_sequence(gene="TP53")       # gene symbol or Ensembl ID -> handle
fetch_region(region="chr11:5,225,000-5,235,000")   # coordinates -> handle
fetch_gene_for_expression(gene="HBB")     # TSS-centred 9,198 bp handle for expression

# 2. Predict against the handle:
predict_promoter(sequence_ref=<ref>)
predict_expression(sequence_ref=<ref>, description="K562 cells")
predict_splice(sequence_ref=<ref>)        # + predict_enhancer / predict_chromatin

# 3. Annotation on MCP is `find_genes` (there is no predict_annotation).
#    It takes a handle, not a region, and runs async internally:
find_genes(sequence_ref=<ref>)            # wait=True (default) returns the result
find_genes(sequence_ref=<ref>, wait=False)  # -> job_id; poll get_job(job_id)

# Discover models with list_models(task); reference context lives in the
# gi://models, gi://docs/tasks, and gi://account MCP resources.

Composite: find genes, then predict expression

To answer "what genes are in this region and how are they expressed?", use the composite:

• MCP: find_genes_and_predict_expression(sequence_ref=..., description=...) — takes a handle, not a region (acquire one with fetch_region first); description is required. Finds genes in the sequence and returns an expression prediction for each.
• REST: call gene discovery, then loop expression per gene (build each TSS-centred 9,198 bp window via the acquisition helpers).

Errors

| Code | Meaning | Action | |---|---|---| | 400 | Invalid request / bad sequence | Check the body; expression must be exactly 9,198 bp and carry description | | 401 | Missing/invalid key (REST) | Set GI_API_KEY; or use the keyless MCP demo | | 413 | Sequence too long | Stay within the task's length bound (≤500,000 bp) | | 429 | Rate / concurrency cap | Back off and retry; ask GI to raise your tier | | 422 | Validation failed (validation_failed) | The most common failure: expression not exactly 9,198 bp, or a sequence below the model's minimum length | | 5xx | Server error | Retry; if persistent, contact support |

Reference files

• references/tasks.md — per-task output shapes, model registries, the async annotation contract.
• references/api-and-auth.md — REST endpoints, the {data, meta} envelope, auth, base-URL override, tiers.
• references/mcp.md — the hosted MCP tool list, the handle-based flow, and the gi:// resources.
• references/sequence-acquisition.md — Ensembl fetch calls and the expression-window (9,198 bp, TSS-centred) math.