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:

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 JSON request above that length is 413 sync_too_large — 200,000 bp on annotation and 50,000 bp on the composite workflow in contract revision 16. Read the field rather than memorising the numbers. Annotation BED/GFF3 stays synchronous at any admitted length and can time out; the other five predict tasks have no hard sync cap.

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 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 submitted length is meta.sequence_length; the scored width is always 9,198, i.e. scored_window[1] - scored_window[0]. In revision 16, data.input contains only sequence_name, description, and tss_index; it does not contain the submitted length or scored window. 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 acquire public reference sequence (no key), using a bundled coordinate catalog, cache, UCSC, or Ensembl; retain the returned provenance. 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

The following transport recipe was tested with mocked responses, not authenticated inference. Supply a task-appropriate seq before calling it. Use the exact expression-context wording consistently when comparing predictions.

import os
import time
import requests

BASE = os.environ.get("GI_BASE_URL", "https://api.genomicintelligence.ai").rstrip("/")
HEADERS = {"Authorization": f"Bearer {os.environ['GI_API_KEY']}"}
TASKS = {"promoter", "splice", "enhancer", "chromatin", "annotation", "expression"}

def predict(task, sequence, sequence_name, model=None, options=None, tss_index=None):
    if task not in TASKS:
        raise ValueError("Unknown GI task")
    body = {"sequence": sequence, "sequence_name": sequence_name}
    if model is not None:
        body["model"] = model
    if options is not None:
        body["options"] = options
    if tss_index is not None:
        if task != "expression":
            raise ValueError("tss_index is expression-only")
        body["tss_index"] = tss_index
    r = requests.post(f"{BASE}/v1/tasks/{task}/predict", headers=HEADERS,
                      json=body, timeout=(10, 300))
    r.raise_for_status()
    if r.status_code != 200:
        raise RuntimeError(f"Unexpected prediction status {r.status_code}")
    return r.json()

# After acquiring and checking an appropriate promoter sequence:
# out = predict("promoter", seq, "TP53_region")
# print(out["meta"]["task_specific_counts"]["regions_found"])

# A validated gene-sense expression window, or longer locus with known TSS:
# out = predict("expression", locus_seq, "HBB", tss_index=tss_offset,
#               options={"description": "polyA plus RNA-seq; Homo sapiens K562"})
# assert out["meta"]["sequence_length"] == len("".join(locus_seq.split()))
# assert out["meta"]["task_specific_counts"]["scored_window"] == [tss_offset-4599, tss_offset+4599]
# print(out["data"]["prediction"]["expression_log_tpm"])

data.summary is for display: its keys may change without a contract revision. Use the declared fields in data and meta.task_specific_counts for computation. A timeout or proxy error may have a non-JSON body; it does not establish that the inference never ran. Preserve the request ID and avoid blind POST resubmission.

Async (any task; recommended for annotation)

Send Prefer: respond-async on any of the six tasks or the composite. A 202 is {data: {job_id, status: "accepted", links}, meta}. Content-Location and X-Job-Id identify the same job. Async is JSON-only; text format plus async is 400. Save the job ID before polling. This bounded polling example surfaces HTTP failures (including 429 and 410) for the caller to handle:

def submit_annotation(sequence, sequence_name):
    r = requests.post(f"{BASE}/v1/tasks/annotation/predict",
                      headers={**HEADERS, "Prefer": "respond-async"},
                      json={"sequence": sequence, "sequence_name": sequence_name},
                      timeout=(10, 30))
    r.raise_for_status()
    if r.status_code != 202:
        raise RuntimeError(f"Unexpected submission status {r.status_code}")
    return r.json()["data"]["job_id"]

def wait_for_job(job_id, max_polls=120):
    if max_polls < 1:
        raise ValueError("max_polls must be positive")
    for attempt in range(max_polls):
        r = requests.get(f"{BASE}/v1/tasks/jobs/{job_id}", headers=HEADERS,
                         timeout=(10, 30))
        r.raise_for_status()  # failed job -> its underlying 4xx/5xx, not 200
        if r.status_code == 200:
            return r.json()
        if r.status_code != 202:
            raise RuntimeError(f"Unexpected polling status {r.status_code}")
        if attempt + 1 < max_polls:
            time.sleep(5)
    raise TimeoutError(f"Polling stopped; resume this job rather than resubmit: {job_id}")

# job_id = submit_annotation(seq, "TP53_region")  # persist this ID
# result = wait_for_job(job_id)
# assert result["data"]["task"] == "annotation"
# transcripts = result["data"]["transcripts"]

200 is completion; 202 contains data.status and data.progress. Unknown/not-owned jobs are 404; expired jobs are 410 job_expired. Results are documented as retained 24 hours from last activity; save results locally. Job listing is a recent, bounded list, not a paginated archive.

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 data.ref, passed as sequence_ref):
load_demo_sequence(name="promoter_tp53")  # keyless smoke test; name is required
fetch_ensembl_sequence(gene="TP53", flank_bp=5000)  # include regulatory context
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)  # own key only -> job_id; poll get_job(job_id)

# Acquisition returns data.ref; use that value as sequence_ref.
# Discover models with list_models(task); reference context lives in the
# gi://models, gi://docs/tasks, and gi://account MCP resources.

The shared demo disables get_job, list_jobs, and detached wait=False. Keep wait=True there; a wait timeout is an error, not a recoverable job handle. See MCP details for resources, result envelopes and lifetimes.

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: one call — POST /v1/workflows/find-genes-and-predict-expression, body {sequence, options} with sequence 1,000–500,000 bp and options.description (cell type / assay) required; a missing or empty description is a 422 validation_failed. It annotates, centres a 9,198 bp window on each discovered gene's TSS (padding with N up to half the window rather than dropping an edge gene), and returns a prediction per gene. meta.task_specific_counts = {genes_found, genes_predicted, genes_skipped} with genes_predicted + genes_skipped == genes_found; per-gene causes in data.expression_predictions[].skip_reason. Above 50,000 bp (its x-sync-limit-bp) it forces async: a synchronous request over that size is 413 sync_too_large with error.details = {sequence_length, threshold} — retry the same body with Prefer: respond-async.

The API also publishes a separate, under-development VCF workflow. Its outputs are not established model results when meta.model is absent; see the bounded contract note.

Errors

error.code is a closed 21-value enum (bad_request, unauthorized, forbidden, not_found, conflict, job_expired, payload_too_large, sync_too_large, unsupported_format, validation_failed, too_many_requests, rate_limited, internal_error, timeout, insufficient_memory, model_not_found, task_not_supported_by_model, model_loading, service_unavailable, http_error, unknown); treat an unlisted value as a generic failure, not a parse error.

**Branch first on code, never on message text or loc.** Pydantic request failures usually carry details.errors; the splice response cap instead carries record_count, maximum_records, sequence_length, and threshold. Handle these as distinct optional detail shapes. More than 20,000 splice records causes 422 validation_failed, not a truncated result; raise the threshold and record that changed analysis setting. See task caveats.

For correlation, error.request_id and the X-Request-Id header are both documented on API responses, and success envelopes carry meta.request_id. Reading the header first remains a safe default. API responses document RateLimit-Limit, RateLimit-Remaining, RateLimit-Reset, RateLimit-Policy; a 429 adds Retry-After. The limit is a burst bucket, not rpm: the published x-rate-limit-burst-divisor is 6, so the sustained minute allowance is six times that header. Proxy failures may omit these headers and the usual JSON error envelope.

Reviewed 2026-10-01 against live OpenAPI info.version 2026.09.22.2 (af902d84), x-contract-revision: 16, and gi-mcp 0.1.0a21. Record the contract revision, resolved model ID, assembly, strand/TSS provenance, options, and experimental description with results. Hash the exact submitted bases when available. A handle-only MCP acquisition returns a preview, not the full bases or a checksum: preserve its acquisition parameters and source release metadata, and do not invent a hash or claim byte-level verification. Review evidence and limits distinguish public discovery, source review, and mocked examples from inference validation.

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, including tss_index.