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.
Instrucciones de origen · Vista previa de solo lectura
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 capped public demo quota, key optional.
metadata
{"version":"1.0","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":[{"name":"GI_API_KEY","required":false,"description":"Optional gi_ bearer key for the REST /v1 API and a higher MCP quota. The hosted MCP demo runs keyless; request a key at contact@genomicintelligence.ai."}]}}
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.
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 below and
.
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
The hosted MCP demo is keyless — try it with nothing set.
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 RESTexport 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']}"}
defpredict(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 limitreturn 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"]
whileTrue:
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.
