---
name: gi-expression-bb52b89493ed
description: Predict tissue / cell-type expression (log TPM + TPM) from 9,198–500,000 bp of DNA around a TSS, at least 4,599 bp each side (anything but exactly 9,198 bp needs --tss-index) using the Genomic Intelligence G0 Expression model, via the hosted /v1/tasks/expression/predict
  API. The model is conditioned on a free-text cell-type / assay description.
license: MIT
metadata:
  openclaw:
    requires:
      bins:
      - python3
      env: null
      config: null
    always: false
    emoji: 🧪
    homepage: https://docs.genomicintelligence.ai
    os:
    - darwin
    - linux
    install:
    - kind: pip
      package: requests
      bins: null
    trigger_keywords:
    - expression prediction
    - predict expression
    - sequence to expression
    - TPM prediction
    - cell type expression
    - tissue expression
    - RNA-seq prediction
    - gi expression
    - G0 expression
    - genomic intelligence expression
  author: ClawBio + Genomic Intelligence
  demo_data:
  - path: example_data/expression_hbb_k562.fa
    description: HBB (β-globin), 9,198 bp centred on the TSS, reverse-complemented to gene-sense. K562 is the demo cell context — HBB is highly expressed in K562 erythroleukemia.
  dependencies:
    python: '>=3.10'
    packages:
    - requests>=2.31
  domain: genomics
  endpoints:
    cli: python skills/gi-expression/gi_expression.py --input {input_file} --output {output_dir}
  inputs:
  - name: input_file
    type: file
    format:
    - fa
    - fasta
    - fna
    description: Single-record FASTA, gene-sense (RC minus-strand genes). Either exactly 9,198 bp centered on the TSS, or up to 500,000 bp with --tss-index giving the 0-based TSS offset, at least 4,599 bp from each end.
    required: false
  outputs:
  - name: report
    type: file
    format: md
    description: Markdown report — predicted log(TPM+1), TPM, model + timing.
  - name: result
    type: file
    format: json
    description: Full `{data, meta}` response.
  - name: reproducibility
    type: directory
    description: command.sh + environment.json.
  tags:
  - genomics
  - expression
  - RNA-seq
  - TPM
  - sequence-to-expression
  - dna-lm
  - gi-api
  version: 0.1.0
---

# 🧪 gi-expression

You are **gi-expression**, a ClawBio agent that calls the **Genomic Intelligence** sequence-to-expression model. Given at least 9,198 bp around a TSS (`--tss-index` unless it is exactly 9,198 bp) and a cell-type description, it returns predicted expression (log TPM + TPM).

> ⚠️ **Remote inference — opt-in required.** Unlike most ClawBio skills, this skill uploads your FASTA sequence to the hosted Genomic Intelligence API at `https://api.genomicintelligence.ai`. The same models also run interactively at <https://genomicintelligence.ai>. **Do not submit identifiable patient data** without an appropriate data-use agreement. Key setup: see [Authentication](#authentication) below.

## Trigger

**Fire this skill when the user says any of:**
- "predict expression for this gene / sequence"
- "what's the expression of this region in [cell type]?"
- "sequence-to-expression prediction"
- "TPM prediction", "log TPM prediction"
- "gi-expression", "G0 expression"

**Do NOT fire when:**
- The user has counts / RNA-seq output and wants differential expression → `rnaseq-de`
- The user wants tissue annotation / GTEx lookup → use external resources

## Why This Exists

- **Without it**: Sequence-to-expression models (Enformer / Borzoi / G0 Expression) need GPU + private weights + careful 9-kbp windowing.
- **With it**: One CLI call → expression prediction conditioned on free-text cell-type description, in <1 s.
- **Why ClawBio**: Private weights, hosted. ClawBio's reproducibility bundle + chaining (`gi-promoter` → `gi-expression` → `rnaseq-de` interpretation).

## API Backed

`POST https://api.genomicintelligence.ai/v1/tasks/expression/predict`. Omit `model` and the API resolves the default; `GET /v1/tasks/expression/models` is the current list.

> **Contract note.** The Genomic Intelligence API publishes one operation per task, each with its own request schema: per-task `minLength`/`maxLength` on `sequence`, and a typed, closed `options` object (an unknown option key is a `422 validation_failed`, not a silent ignore). The bounds quoted in this file are the published ones, but the authority is always the served schema: `GET https://api.genomicintelligence.ai/v1/openapi.json`.

## Workflow

1. **Parse**: single-record FASTA, gene-sense. Either exactly 9,198 bp TSS-centered, or 9,198–500,000 bp with `--tss-index` at least 4,599 bp from each end. Anything else is rejected locally before the request is sent.
2. **Build options**: `{"description": "assay term name is polyA plus RNA-seq. biosample summary is Homo sapiens K562."}` by default; override via `--description "..."`.
3. **POST** to `/v1/tasks/expression/predict`, which is its own operation with its own request schema — each of the six tasks has one, so there is no shared predict body.
4. **Render**: `report.md` (headline log TPM plus the scored window the API actually used) + `result.json` + `reproducibility/`.

## CLI Reference

```bash
# Demo — HBB in K562
python skills/gi-expression/gi_expression.py --demo --output /tmp/gi-expression-demo

# Custom cell-type description
python skills/gi-expression/gi_expression.py \
  --input my_tss_window.fa \
  --description "assay term name is polyA plus RNA-seq. biosample summary is Homo sapiens liver." \
  --output report_dir

# Whole locus: the model reads around --tss-index
# (0-based offset into the sequence, counted after whitespace is stripped)
python skills/gi-expression/gi_expression.py \
  --input my_locus_50kb.fa --tss-index 24000 \
  --output report_dir

# Via ClawBio runner
python clawbio.py run gi-expression --demo
```

## Authentication

The skill requires a Genomic Intelligence partner key in `GI_API_KEY`. Resolution order:

1. `--api-key <value>` CLI flag (explicit override).
2. `GI_API_KEY` environment variable.
3. Otherwise: the skill raises a `RuntimeError` pointing here.

### Quick start — ClawBio hackathon key

A shared hackathon-tier key ships in `.env.example` at the repo root (opt-in only). Caps are per-key and are not published as a fixed number — read `RateLimit-Limit` / `RateLimit-Remaining` on any `/v1/tasks/` response for the live allowance. The runner keeps them for you: they are in `result.json` under `rate_limit`, and a `429` names them on the error line. From wherever the ClawBio files live on your machine:

```bash
# Repo root (git clone) — or ~/.claude/plugins/cache/clawbio/clawbio/<version>/ for plugin installs
cp .env.example .env
set -a && source .env && set +a
```

### Production / heavier use

Request an individual key at **contact@genomicintelligence.ai**, then:

```bash
export GI_API_KEY=gi_yourkeyhere
```

## Demo

```bash
python clawbio.py run gi-expression --demo
```

Bundled fixture is HBB centered on its canonical TSS, RC'd to gene-sense, scored with the skill's default K562 description.

> Read the predicted value from your own run rather than from this page. Absolute predictions move when the model checkpoint changes, so any figure written here becomes a false claim. What is stable is the *relative* signal — gene-sense scores far above the genomic strand, and highly-expressed genes score above silent ones in the same cell context. Do not build assertions on an absolute value read from documentation.

## Gotchas

- **9,198 bp is a floor, not a fixed size.** The endpoint accepts 9,198–500,000 bp (`minLength` / `maxLength` on `ExpressionPredictRequest`, counted after whitespace is stripped) with the TSS at least 4,599 bp from each end. Submit exactly 9,198 bp TSS-centered, or a longer locus plus `--tss-index`. How far the model reads is its own: `bio_spec.recommended_flank_bp` on `GET /v1/tasks/expression/models` is how much to fetch on each side of the TSS (4,599 bp for the default `g0-expression`, 40,960 bp for `g0-expression-8192`), and the response reports the part it used as `scored_window`, which is only 9,198 bp wide for `g0-expression`. Anything shorter than 9,198 bp, longer than 500,000 bp, or missing `--tss-index` on a non-9,198 bp sequence is a `422 validation_failed` — the skill catches all of those locally first. Over-max is a 422, *not* a 413; 413 is the separate 16 MiB raw-body cap. A TSS closer than 4,599 bp to either end is rejected rather than padded, and there is no opt-out flag.
- **A `tss_index` error reports at `loc: ["body"]`, never `body.tss_index`.** Both TSS checks are a whole-body validator, so any client branching on the error `loc` will silently never match. Match on `error.code` (`validation_failed`) and use `message` for display only — and read `error.details` defensively: for a validation failure it is the declared `{errors: [{loc, msg, type}, …]}` object.
- **A wrong `--tss-index` does not error — it lies.** Any offset in `[4599, len-4599]` is legal, so an offset computed against file characters (line-wrapped FASTA newlines) or against a chromosome coordinate instead of an offset into *this* sequence returns a confident number for the wrong window. Always check the "Scored window" line in `report.md`. The response reports the applied window as `meta.task_specific_counts.scored_window`; older responses also echoed it as `data.input.scored_window`, and this skill falls back to that echo only for them. The submitted length is a separate field, not part of the window: it is `meta.sequence_length`. Offsets are counted on the whitespace-stripped nucleotide string, so compute the offset against that rather than against the raw file. The parser refuses any base outside `ACGTN`, so the two differ only by whitespace.
- **Gene-sense is mandatory.** Minus-strand genes need reverse-complementing. On the bundled HBB fixture the genomic strand scores about an order of magnitude below gene-sense, though the absolute values move with the checkpoint. The wrong strand returns a well-formed low number, not an error.
- **`description` wording changes the answer.** It is a free-text conditioning input, not an enum, so paraphrases are not equivalent: on the same fixture and the same sequence, `"K562"`, `"K562 cells"` and the canonical assay-format string give three different predictions, spanning roughly a factor of two in TPM. Pick one phrasing and keep it fixed across anything you intend to compare, and prefer the canonical `"assay term name is … biosample summary is …"` format the model was trained on.
- **`description` is required** — in the published schema as well as at runtime, and it is the *only* key accepted inside expression `options`. The model is conditioned on it; "assay term name is polyA plus RNA-seq. biosample summary is Homo sapiens [tissue]." is the canonical format.
- **TPM scale is not absolute** across tissues — useful as a relative ranking within a cell type, not as a precise count prediction.
- **Hackathon key is shared** — `GI_API_KEY` for heavier use.

## Output Structure

```
output_dir/
├── report.md
├── result.json
└── reproducibility/
    ├── command.sh
    └── environment.json
```

## Integration with Bio Orchestrator

Routes here on: "predict expression", "sequence to expression", "TPM prediction", "cell-type expression".

Chains with: `gi-promoter` → `gi-expression` (validate predicted promoters by predicting downstream expression), `rnaseq-de` (compare predicted expression to measured DE results), `variant-annotation` (compare ref/alt sequence expression for promoter / 5'UTR variants).

## Safety

Research and development use. Not for clinical or diagnostic decisions. Predictions are model outputs, not measurements.


---
Source attribution: ClawBio
Original project: https://github.com/ClawBio/ClawBio
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: ClawBio
Original project: https://github.com/ClawBio/ClawBio
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Education & Research, Data & Analytics, Development & Engineering, Healthcare & Life Sciences
Roles: Researcher, Data Scientist, ML / AI Engineer, Business Intelligence Analyst, Health Informatics
Specialties: API
Tools: Genomic Intelligence Api
Country applicability: Country not specified
Classification basis: Source category, role and specialization labels
Instruction SHA-256: 682776ec66f14979abeb8b72bece55407dba95aae83c39b730d9886bde8bfc85