mccnado 0.1.3

MCCNado: Rust-based tools for use in processing Micro-Capure-C data using SeqNado

MCCNado

A high-performance Rust library with Python bindings for processing Micro-Capture-C (MCC) sequencing data.

Overview

MCCNado is a bioinformatics tool designed for analyzing chromatin conformation capture sequencing data. It provides efficient implementations for common preprocessing tasks including FASTQ deduplication, viewpoint read splitting, BAM annotation, and ligation junction analysis.

Features

• FASTQ Deduplication: Remove duplicate reads from single-end and paired-end FASTQ files
• Viewpoint Read Splitting: Split reads containing viewpoint sequences into constituent segments
• BAM Annotation: Add metadata tags to BAM files for downstream analysis
• Ligation Junction Identification: Extract and analyze chromatin interaction data
• Ligation Statistics: Generate comprehensive statistics on cis/trans interactions
• High Performance: Implemented in Rust with optional async processing for large datasets

Installation

From PyPI (recommended)

pip install mccnado

From Source

git clone https://github.com/yourusername/MCCNado.git
cd MCCNado
pip install .

Development Installation

git clone https://github.com/yourusername/MCCNado.git
cd MCCNado
pip install -e .

Requirements

• Python 3.7+
• Rust (for building from source)
• samtools (for BAM file processing)

Usage

Python API

import mccnado

# Deduplicate FASTQ files
stats = mccnado.deduplicate_fastq(
    fastq1="input_R1.fastq.gz",
    output1="output_R1.fastq.gz",
    fastq2="input_R2.fastq.gz",  # Optional for paired-end
    output2="output_R2.fastq.gz"  # Optional for paired-end
)

print(f"Total reads: {stats['total_reads']}")
print(f"Unique reads: {stats['unique_reads']}")
print(f"Duplicate reads: {stats['duplicate_reads']}")

# Split viewpoint reads
mccnado.split_viewpoint_reads(
    bam="aligned_reads.bam",
    output="split_reads.fastq.gz"
)

# Annotate BAM file with MCC metadata
mccnado.annotate_bam(
    bam="input.bam",
    output_directory="annotated_output/"
)

# Extract ligation statistics
mccnado.extract_ligation_stats(
    bam="annotated.bam",
    stats="ligation_stats.json"
)

Command Line Interface

The package also provides a command-line interface through the Python module:

# Deduplicate FASTQ files
python -m mccnado.cli deduplicate input_R1.fastq.gz output_R1.fastq.gz

# Split viewpoint reads
python -m mccnado.cli split-reads aligned_reads.bam split_reads.fastq.gz

# Annotate BAM files
python -m mccnado.cli annotate input.bam output_directory/

# Extract ligation statistics
python -m mccnado.cli ligation-stats annotated.bam stats.json

File Formats

Input Files

• FASTQ: Raw sequencing reads (single-end or paired-end, gzipped or uncompressed)
• BAM: Aligned reads with proper headers and indexing

Output Files

• FASTQ: Deduplicated reads
• BAM: Annotated alignment files with MCC-specific tags
• JSON: Ligation statistics and metadata

BAM Tags Added by MCCNado

• VP: Viewpoint name
• OC: Oligo coordinates
• RT: Reporter tag (0 for capture reads, 1 for reporter reads)

Performance

MCCNado is optimized for large-scale data processing:

• Memory Efficient: Streaming processing for large files
• Parallel Processing: Multi-threaded operations where applicable
• Fast Hashing: Uses xxHash for rapid duplicate detection
• Batch Processing: Configurable batch sizes for optimal performance

Architecture

The package consists of several core modules:

• fastq_deduplicate: FASTQ deduplication logic
• viewpoint_read_splitter: Read segmentation functionality
• mcc_data_handler: BAM annotation and processing
• ligation_stats: Statistical analysis of ligation events
• utils: Common utilities and data structures

Development

Building from Source

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Clone and build
git clone https://github.com/yourusername/MCCNado.git
cd MCCNado
cargo build --release

# Install Python package
pip install -e .

Running Tests

# Rust tests
cargo test

# Python tests
python -m pytest tests/

Contributing

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

License

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

Citation

If you use MCCNado in your research, please cite:

[Your Citation Here]

Support

For questions, issues, or feature requests, please:

1. Check the documentation
2. Search existing issues
3. Open a new issue if needed

Acknowledgments

• Built with PyO3 for Python-Rust interoperability
• Uses noodles for bioinformatics file format handling
• Powered by tokio for async operations