🧬 Bioutils Collection

Production-ready bioinformatics toolkit - 77+ optimized functions for sequence analysis, alignment, annotation, and molecular biology workflows.

✨ Highlights

• 🚀 77+ specialized functions across 13 bioinformatics domains
• 🔒 Fully typed with complete type hints (mypy strict)
• 📊 Research-grade algorithms - Needleman-Wunsch, Smith-Waterman, and more
• ⚡ Performance optimized for large-scale genomic data
• ✅ Extensively tested with comprehensive test coverage
• 📝 Self-documenting - NumPy-style docstrings with examples

📦 Installation

pip install bioutils-collection

Requirements: Python 3.10+ with numpy, scipy, and scikit-learn

🎯 Quick Start

from bioutils_collection import (
    reverse_complement,
    gc_content,
    needleman_wunsch,
    parse_fasta,
    translate_dna_to_protein,
)

# Sequence manipulation
seq = "ATCGATCG"
rev_comp = reverse_complement(seq)  # "CGATCGAT"

# Calculate GC content
gc = gc_content("ATCGATCG")  # 0.5

# Global sequence alignment
seq1, seq2 = "GATTACA", "GCATGCU"
aligned1, aligned2, score = needleman_wunsch(seq1, seq2)

# Parse FASTA files
for header, sequence in parse_fasta("genome.fasta"):
    print(f"{header}: {len(sequence)} bp")

# Translate DNA to protein
protein = translate_dna_to_protein("ATGGCCTAA")  # "MA*"

🧬 Modules

Core Sequence Operations

• alignment_functions - Pairwise & multiple sequence alignment (Needleman-Wunsch, Smith-Waterman, BLAST score ratio)
• sequence_operations - Reverse complement, ORF finding, CpG islands, low-complexity filtering
• translation_functions - DNA↔RNA transcription, translation with custom codon tables

Sequence Analysis & Statistics

• gc_functions - GC content, GC skew, windowed GC profiling
• sequence_statistics - Codon usage (CAI, ENC, RSCU), melting temp, isoelectric point, amino acid composition
• data_validation - DNA/RNA/protein sequence validation

File I/O & Parsing

• fasta_misc - FASTA parsing, writing, filtering, splitting, concatenation, primer generation
• annotation_functions - BED/GFF/GTF/VCF parsing and conversion, annotation statistics

Pattern & Motif Discovery

• motif_functions - Motif search, consensus generation, pattern matching
• repeat_functions - Tandem repeat finder, palindrome detection
• restriction_functions - Restriction enzyme site identification
• clustering_functions - Motif clustering and grouping

�🔬 Use Cases

Genomic Analysis

from bioutils_collection import find_orfs, gc_content_windows, find_cpg_islands

# Find all ORFs in a sequence
orfs = find_orfs(dna_sequence, min_length=300)

# Sliding window GC analysis
gc_proDevelopment

```bash
# Clone repository
git clone https://github.com/MForofontov/bioutils-collection.git
cd bioutils-collection

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

# Run tests
pytest

# Run specific test categories
pytest -m alignment
pytest -m fasta
pytest -m translation

# Type checking
mypy bioutils_collection

# Linting
ruff check .

# Coverage report
pytest --cov=bioutils_collection --cov-report=html

📚 API Documentation

All functions include:

• ✅ Complete type hints for static analysis
• 📖 NumPy-style docstrings with parameter descriptions
• 💡 Usage examples in docstrings
• ⚠️ Complexity notes for performance-critical code
• 📎 Algorithm references where applicable

Example:

from bioutils_collection import needleman_wunsch
help(needleman_wunsch)  # Comprehensive documentation

🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Add comprehensive tests
4. Ensure all tests pass (pytest)
5. Add type hints and docstrings
6. Submit a pull request

Development Guidelines:

• Follow existing code style (ruff formatting)
• Add tests for all new functions
• Update documentation
• Keep functions focused and single-purpose

🔗 Related Projects

• BioPython - Comprehensive biological computation library
• scikit-bio - Scientific Python library for bioinformatics
• pyutils-collection - General Python utilities (sister project)

Protein properties

pi = calculate_isoelectric_point(protein_seq) composition = amino_acid_composition(protein_seq)

Primer design

tm = melting_temperature("ATCGATCGATCG")

## 🧪 Testing

```bash
# Run all tests
pytest

# Run specific module tests
pytest -m alignment
pytest -m fasta
pytest -m translation

# Run with coverage
pytest --cov=bioutils_collection --cov-report=html

📖 Documentation

Each function includes:

• Complete type hints for static analysis
• NumPy-style docstrings with parameter descriptions
• Usage examples in docstrings
• Complexity notes for performance-critical code
• Algorithm references where applicable

📊 Project Stats

• 77+ Functions across 13 specialized modules
• 670+ Tests with comprehensive coverage
• Type-safe with mypy strict mode
• Python 3.10+ with modern type hints

📮 Contact & Support

• Author: Mykyta Forofontov
• Repository: github.com/MForofontov/bioutils-collection
• Issues: Report bugs or request features
• PyPI: pypi.org/project/bioutils-collection

Star this repo if you find it useful!

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

📄 License

MIT © 2026 Mykyta Forofontov

See LICENSE for the full license text.

🔗 Related Projects

• BioPython - Comprehensive bioinformatics toolkit
• scikit-bio - Scientific Python library for bioinformatics

📮 Contact

Author: Mykyta Forofontov
Repository: https://github.com/MForofontov/bioutils-collection
Issues: https://github.com/MForofontov/bioutils-collection/issues