---
name: omics-target-evidence-mapper
description: Aggregate public target-level evidence across omics and translational sources for research triage.
version: 0.1.0
tags:
  - omics
  - targets
  - translational-research
  - literature
  - trials
metadata:
  openclaw:
    requires:
      bins:
        - python3
      env: []
      config: []
    always: false
    emoji: "🧬"
    homepage: https://github.com/ClawBio/ClawBio
    os: [darwin, linux]
    install: []
    trigger_keywords:
      - target evidence
      - gene disease evidence
      - target triage
      - omics evidence
      - gene disease mapper
---

# Why This Exists

Researchers often need a quick first-pass view of whether a gene or protein target has evidence across multiple public sources. In practice, this usually means checking several websites manually and informally combining results. This skill makes that process reproducible by retrieving and organising public evidence into one structured output.

This skill is for research triage only. It does not infer causality, rank therapeutic value, or make clinical recommendations.

# Core Capabilities

1. Accept a gene or protein target and an optional disease term.
2. Retrieve canonical target information from UniProt.
3. Retrieve disease-target association evidence from Open Targets.
4. Retrieve recent literature hits from PubMed.
5. Optionally retrieve trial records relevant to the target and disease.
6. Produce a machine-readable JSON file and a human-readable Markdown report.

# Input Formats

| Argument | Required | Example | Notes |
|---|---|---|---|
| `--gene` | Yes, unless `--demo` is used | `IL6R` | Gene or target symbol |
| `--disease` | No | `coronary artery disease` | Optional disease context |
| `--output` | Yes | `demo_out` | Output directory |
| `--max-papers` | No | `5` | Number of PubMed hits to include |
| `--max-trials` | No | `5` | Number of trial records to include |
| `--demo` | No | `--demo` | Runs the built-in demo query |

# Workflow

1. Validate CLI inputs.
2. Resolve the query from either user input or demo mode.
3. Query public data sources.
4. Normalise results into a structured evidence object.
5. Write JSON and Markdown outputs.
6. Write file checksums for reproducibility.

# CLI Reference

## Demo mode

```bash
python skills/omics-target-evidence-mapper/omics_target_evidence_mapper.py --demo --output demo_out



---
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 SkillsHub Contributors

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: Explicit original project field in package

Fields of work: Data & Analytics, Education & Research
Roles: Business Intelligence Analyst, Data Scientist, Researcher
Specialties: API
Tools: Open Targets, Pubmed, Python3, Uniprot
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: cac15b380bd243049c66862be6b2050c783ea6810a80e7fcab8460fe6919cc46
Metadata reviewed: 2026-10-27
This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.