Aptamer Generator

A Python package for generating DNA aptamer candidates with controlled GC content using AI-driven methods.

Features

• Configurable Generation: Control sequence length and GC content ranges
• Reproducibility: Seed-based random number generation for consistent results
• Validation: Built-in GC content validation using Biopython
• Testing: Full test coverage with pytest
• Packaging: Easy installation via pip/PyPI

Installation

From GitHub

pip install git+https://github.com/avinab-neogy/aptamer_generator.git

Local Development

git clone https://github.com/avinab-neogy/aptamer_generator.git
cd aptamer_generator
pip install -e .[test] # Editable mode with test dependencies

Usage

Basic Example

from aptamer_generator import AptamerGenerator

# Initialize generator with seed

generator = AptamerGenerator(seed=42)

# Generate 5 sequences of length 40 with 45-55% GC

sequences = generator.generate_candidates(
num=5,
length=40,
gc_range=(0.45, 0.55)
)

# Print results

for i, seq in enumerate(sequences, 1):
print(f"Sequence {i}: {seq}")

Development

Running Tests

pytest -v --cov=aptamer_generator --cov-report=term-missing

Building the Package

python -m build

Uploading to PyPI (Optional)

twine upload dist/*

Technical Details

Dependencies

• Python >=3.8
• numpy >=1.21
• biopython >=1.79

Algorithm

Uses rejection sampling with the following steps:

1. Random DNA sequence generation
2. GC content calculation
3. Constraint validation
4. Repeat until requested number of valid sequences are generated

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin feature/your-feature)
5. Open a Pull Request

License

MIT License - see LICENSE for details