genomic-intelligence

name	genomic-intelligence
description	Predict regulatory features, gene structure, and expression directly from DNA sequence using Genomic Intelligence's hosted transformer DNA language models — no local GPU or model weights. Six tasks over a REST API and a hosted MCP server (keyless public demo): promoter regions, splice donor/acceptor sites, enhancer activity, chromatin state, sequence-to-expression (log TPM), and de-novo gene annotation, plus a composite find-genes-then-predict-expression workflow. Use when the user has a gene symbol, a genomic region, or a DNA/FASTA sequence and wants any of these predictions, mentions Genomic Intelligence, genomicintelligence.ai, api.genomicintelligence.ai, or mcp.genomicintelligence.ai.
license	MIT
compatibility	Python 3.10+ with the `requests` library for the REST path (no dedicated SDK). Network access required. The REST `/v1` API needs a `GI_API_KEY` (a `gi_` bearer); the hosted MCP server at mcp.genomicintelligence.ai/mcp works keyless against a rate- and concurrency-limited public demo tier, key optional.
metadata	{"version":"1.2","skill-author":"Genomic Intelligence","trigger-keywords":"DNA sequence prediction, regulatory genomics, promoter prediction, splice site prediction, enhancer activity, chromatin state, gene expression prediction, sequence to expression, log TPM, gene annotation, transcript prediction, DNA language model, genomic intelligence, hosted inference, Ensembl sequence, FASTA prediction, cis-regulatory, TSS window, DeepSEA, DeepSTARR, BigBird splice, MCP genomics","openclaw":{"primaryEnv":"GI_API_KEY","envVars":["[Truncated]"]}}

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 [e-mail].
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:

Task	Recommended mode	Accepted length	context_window_bp	Notes
promoter	sync	300–500,000 bp	2,000 bp	sliding-window promoter regions
splice	sync	100–500,000 bp	15,000 bp	donor/acceptor sites (long-context BigBird); strand-specific — feed transcript orientation
enhancer	sync	50–500,000 bp	249 bp	dev + housekeeping scores (DeepSTARR, Drosophila)
chromatin	sync	200–500,000 bp	1,000 bp	hundreds of tracks (DeepSEA)
expression	sync	9,198–500,000 bp	n/a (trained_window_bp 9,198)	log(TPM+1); needs tss_index unless exactly 9,198 bp, plus a cell-type description
annotation	async	1,000–500,000 bp	n/a	de-novo transcripts; submit + poll; sync above 200,000 bp is 413 sync_too_large

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:

Task	options keys
promoter	threshold (0–1, default 0.5)
splice	threshold (0–1, default 0.5), site_types (subset of ["donor","acceptor"], default both)
enhancer	(none)
chromatin	threshold (0–1, default 0.5)
annotation	batch_size (1–128, default 8), shift_coordinates, reverse_complement (default true)
expression	description — required, and the only key

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 4599 ≤ tss_index ≤ len(sequence) − 4599. At exactly 9,198 bp it defaults to 4,599, the only legal value there. So you may submit a whole locus (up to 500 kb) and let the server cut the window — but the server does not discover the TSS for you (that is the composite workflow's job), and does not reverse-complement: submit gene-sense sequence.
• options.description — a cell-type / assay string (e.g. "K562 cells") — is required, and is the only key expression accepts inside options. Unknown top-level body fields are rejected too.

Note: the legal tss_index range is wide, so an offset that is merely wrong (counted over raw FASTA characters including newlines, or relative to a locus start rather than the submitted slice) does not error — it returns a confident 200 for the wrong window. Assert on meta.task_specific_counts.scored_window / .tss_index in the response. The length you submitted is meta.sequence_length (also echoed as data.input.submitted_sequence_length); the scored width is always 9,198, i.e. scored_window[1] - scored_window[0]. (data.input.sequence_length was removed at contract revision 13.)

Both tss_index violations — "required unless exactly 9,198 bp" and the range check — come from a whole-model validator, so they surface at the body level rather than under tss_index. Match on error.code == "validation_failed" and use the message for display only. Any loc tuple quoted in this skill is illustrative of that shape, not part of the contract: it is not published in the schema and must not be branched on.

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). Otherwise fetch a wider locus and pass the TSS as tss_index so the server cuts the window — but compute that offset on the stripped nucleotide string, not on file characters.
• 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 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

Called synchronously — the default for every task — a prediction is 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, tss_index=None):
    body = {"sequence": sequence, "sequence_name": sequence_name}
    if model:   body["model"] = model
    if options: body["options"] = options
    if tss_index is not None: body["tss_index"] = tss_index   # expression only
    # Each task is its own published operation, but the URL string is unchanged.
    r = requests.post(f"{BASE}/v1/tasks/{task}/predict", headers=HEADERS, json=body)
    # 422 validation_failed  — sequence under the task floor OR over 500,000 bp,
    #                          bad tss_index, missing options.description,
    #                          or ANY unknown body/options key (options is closed)
    # 401 no/bad key · 404 unknown task · 413 body over 16 MiB · 429 rate limit
    r.raise_for_status()
    return r.json()               # {"data": {...}, "meta": {...}}

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

# Expression — a pre-cut 9,198 bp TSS-centred window (tss_index defaults to 4,599):
out = predict("expression", tss_window_9198bp, "HBB",
              options={"description": "K562 cells"})
print(out["data"]["prediction"]["expression_log_tpm"])

# Expression — a whole locus; the server slices ±4,599 bp around the TSS you name.
# tss_index is 0-based into the whitespace-stripped sequence.
out = predict("expression", locus_seq, "HBB",
              options={"description": "K562 cells"}, tss_index=tss_offset_in_locus)
print(out["meta"]["task_specific_counts"]["scored_window"])   # confirm the window scored

Async (any task; recommended for annotation)

Prefer: respond-async is a declared header parameter on all six predict operations and on the composite. A 202 carries the same {data, meta} envelope as a sync 200, with data = {job_id, status: "accepted", links}; the job id is also in the Content-Location and X-Job-Id response headers. Async is JSON-only — combining it with a text format is rejected. annotation is the task that needs it:

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>)