GenomeHouse

Modular Bioinformatics Toolkit for Sequence Analysis, Parsing, ML, and Visualization

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🚀 Installation

pip install genomehouse

🧬 Features

• Sequence Analysis: Reverse complement, motif search, GC content, translation
• Data Parsing: FASTA/FASTQ, VCF, GFF/GTF, memory-efficient streaming
• Machine Learning: Feature extraction, classification, clustering, cross-validation
• Visualization: Alignment plots, phylogenetic trees, statistical charts, interactive visualizations
• Statistical Analysis: Hypothesis testing, correlation, distribution fitting, significance testing
• Extensible API: Modular architecture, plugin system, custom workflows

📦 Requirements

• Python ≥3.8
• NumPy, Pandas
• Matplotlib, Seaborn
• Scikit-learn

📝 Quick Start

from genomehouse import sequence_tools

# Calculate GC content
seq = "ATGCGTACGGCTA"
gc_content = sequence_tools.gc_content(seq)
print(f"GC Content: {gc_content}%")

# Get reverse complement
rev_comp = sequence_tools.reverse_complement(seq)
print(f"Reverse Complement: {rev_comp}")

# Find motifs
motifs = sequence_tools.find_motifs(seq, "GC")
print(f"GC motifs found at: {motifs}")

💻 CLI Usage

# Parse FASTA file
genomehouse-cli parse-fasta data/sample.fasta

# Calculate GC content
genomehouse-cli gc-content ATGCGTAC

# Convert FASTQ to FASTA
genomehouse-cli convert reads.fastq output.fasta

# Generate sequence statistics
genomehouse-cli stats genome.fasta

📚 Documentation & Resources

• PyPI Package
• API Documentation
• GitHub Repository
• Tutorials
• Examples

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Built with ❤️ for the bioinformatics community by Mubashir Ali and CelloByte Team