LLMomics

LLMomics is an open-source Python library that leverages Large Language Models (LLMs) to automatically generate complete bioinformatics pipelines from natural language queries. It dramatically reduces the technical barrier for researchers who need to perform omics analyses (genomics, transcriptomics, proteomics, metabolomics) without extensive computational expertise.

Features

• Natural Language Interface: Generate pipelines from simple English queries
• Multi-Omics Support: RNA-seq, ChIP-seq, ATAC-seq, variant calling, and more
• Public Database Integration: Automatic data fetching from GEO, SRA, and other repositories
• Production-Ready Pipelines: Generates complete Snakemake workflows with proper logging and error handling
• Extensible Architecture: Easy to add new pipeline templates and data sources
• Smart Query Parsing: Automatically extracts dataset IDs, tools, and parameters from queries

Table of Contents

• Installation
• Quick Start
• Usage Examples
• Configuration
• Architecture
• Contributing
• Citation
• License

Installation

Prerequisites

• Python 3.8 or higher
• OpenAI API key (for LLM functionality)

Install from source

# Clone the repository
git clone https://github.com/allanps/llmomics.git
cd llmomics

# Install in development mode
pip install -e .

# Or install directly
pip install .

Set up environment variables

Create a .env file in your project directory:

# Required
OPENAI_API_KEY=your_openai_api_key_here

# Optional (for enhanced NCBI access)
NCBI_EMAIL=your.email@example.com
NCBI_API_KEY=your_ncbi_api_key_here

Quick Start

Basic Usage

Generate a complete RNA-seq differential expression pipeline with a single command:

llmomics run "Perform differential expression analysis with DESeq2 on GSE123456"

This command will:

1. Parse your natural language query
2. Fetch dataset information from GEO
3. Generate a complete Snakemake pipeline
4. Create all necessary configuration files
5. Provide clear instructions for running the analysis

Search for Datasets

# Search across all databases
llmomics search "RNA-seq liver cancer"

# Search specific database
llmomics search "breast cancer" --database geo --limit 20

Fetch Dataset Information

# Automatically detect database from ID
llmomics fetch GSE123456

# Specify database explicitly
llmomics fetch SRR999999 --database sra

Check Configuration

llmomics config

Usage Examples

Example 1: RNA-seq Differential Expression

llmomics run "Analyze RNA-seq data from GSE200366 to find differentially expressed genes between treatment and control groups using DESeq2"

This generates a complete pipeline including:

• Quality control with FastQC
• Read trimming with Trim Galore
• Alignment with STAR
• Gene counting with featureCounts
• Differential expression with DESeq2
• MultiQC report generation

Example 2: ChIP-seq Peak Calling

llmomics run "Perform ChIP-seq peak calling on H3K27ac data using MACS2"

Example 3: Custom Parameters

llmomics run "RNA-seq analysis with p-value < 0.01 and fold change > 2.5 for GSE123456"

Python API Usage

from llmomics import LLMProvider, DataFetcher, PipelineGenerator

# Initialize components
llm = LLMProvider()
fetcher = DataFetcher()
generator = PipelineGenerator()

# Analyze query
query = "Perform RNA-seq analysis on GSE123456"
analysis = llm.analyze_query(query)

# Fetch dataset
dataset_info = fetcher.fetch("GSE123456")

# Generate pipeline
pipeline_path = generator.generate(
    pipeline_type="rna-seq",
    analysis_type="differential-expression",
    dataset_info=dataset_info
)

Configuration

LLMomics uses a flexible configuration system with the following hierarchy:

1. Environment variables (highest priority)
2. .env file in current directory
3. Default values

Key Configuration Options

Setting	Environment Variable	Default	Description
LLM Provider	LLM_PROVIDER	openai	LLM service to use
OpenAI API Key	OPENAI_API_KEY	Required	Your OpenAI API key
OpenAI Model	OPENAI_MODEL	gpt-4	Model to use
Output Directory	LLMOMICS_OUTPUT_DIR	./pipelines	Where to save pipelines
NCBI Email	NCBI_EMAIL	None	Email for NCBI API

Architecture

graph TD
    A[Natural Language Query] --> B[Query Parser]
    B --> C[LLM Analysis]
    C --> D[Data Fetcher]
    D --> E[Pipeline Generator]
    E --> F[Snakemake Pipeline]
    
    D --> G[GEO Database]
    D --> H[SRA Database]
    C --> I[OpenAI API]

Loading

Core Modules

• core: LLM communication and query processing
• data: Integration with bioinformatics databases
• pipeline: Snakemake pipeline generation

Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Setup

# Clone the repository
git clone https://github.com/allanps/llmomics.git
cd llmomics

# Install in development mode with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black src/

# Run linting
flake8 src/

Documentation

For detailed documentation, visit our documentation site.

Citation

If you use LLMomics in your research, please cite:

@software{llmomics2025,
  title = {LLMomics: LLM-powered bioinformatics pipeline generation},
  author = {Allan Paulo},
  year = {2025},
  url = {https://github.com/allanps/llmomics}
}

Issues and Support

• Report bugs at GitHub Issues

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.