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GenoTEX

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Benchmark for testing LLM agents on gene expression data analysis.

Autonomous AgentsResearch 63Open source
View on GitHub
Updated 2026-06-15
GenoTEX GitHub repository

What is GenoTEX?

GenoTEX is an open-source benchmark created to measure how effectively LLM agents carry out end-to-end gene expression data analysis.

It centers on comprehensive gene-trait association analysis and supplies expert-curated annotations to support consistent scoring.

The resource is intended for researchers and developers building or comparing LLM agents for bioinformatics applications.

Capabilities

evaluate llm agents
end-to-end gene expression analysis
gene-trait association analysis
use expert-curated annotations

What you can build with GenoTEX

Agent Performance Testing

Run standardized evaluations to compare different LLM agents on gene expression analysis pipelines.

Gene-Trait Studies

Apply agents to association analysis tasks using the provided expert-curated annotations for validation.

Benchmarking Workflows

Establish baseline results for end-to-end data analysis performance in genomics research settings.

Install GenoTEX

Quick start
git clone https://github.com/Liu-Hy/GenoTEX.git
      cd GenoTEX
  1. 1Visit the official GenoTEX repository on GitHub.
  2. 2Clone the repository to your local machine.
  3. 3Install required Python dependencies listed in the project files.
  4. 4Download the benchmark datasets and annotation files.
  5. 5Execute the evaluation scripts with your chosen LLM agent.

GenoTEX: pros & cons

Pros

  • +Focuses on complete end-to-end analysis rather than isolated steps.
  • +Includes expert-curated annotations for reliable assessment.
  • +Open-source availability encourages community contributions.
  • +Targets a specialized domain with clear evaluation criteria.

Cons

  • Scope is limited to gene expression data only.
  • Requires access to capable LLMs for meaningful results.
  • Benchmark maintenance depends on ongoing community support.
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Frequently asked questions

It evaluates LLM agents on end-to-end gene expression data analysis including gene-trait associations.

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