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

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](https://docs.genomicintelligence.ai) · REST contract at [api.genomicintelligence.ai/v1/openapi.json](https://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]( 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. 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](https://docs.genomicintelligence.ai/). Request a prediction key 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. 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 JSON 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 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: 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]( async any task recommended for annotation). 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. 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: 200 is completion; 202 contains data.status and data.progress . Unknown/not owned jobs

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