mHiC

Overview: mHi-C (multi-mapping strategy for Hi-C data) is an innovative pipeline designed to leverage reads that align to multiple genomic positions in Hi-C analysis. By implementing a probabilistic framework for assigning multi-mapping reads, mHi-C significantly improves the utilization of sequencing data and enhances the detection of chromatin interactions.

Key Features:

1. Multi-mapping Read Processing:

   • Probabilistic assignment of ambiguous reads
   • Statistical modeling of mapping locations
   • Comprehensive read classification
   • Improved data utilization

2. Flexible Analysis Pipeline:

   • Modular step-by-step workflow
   • Independent script organization
   • User-defined parameter settings
   • Customizable implementation options

3. Performance Optimization:

   • C/C++ accelerated computing
   • Parallel processing support
   • High-performance computing integration
   • Memory-efficient implementation

4. Quality Control and Validation:

   • Statistical significance assessment
   • Resolution-specific analysis
   • Comprehensive quality metrics
   • Robust validation methods

Applications: mHiC has been successfully applied to:

• Analyze repetitive genomic regions
• Improve chromatin interaction detection
• Enhance Hi-C data utilization
• Generate high-resolution contact maps
• Study complex genomic architectures

The package is implemented in Python with C/C++ acceleration for computationally intensive components, providing comprehensive documentation and tutorials to support researchers in the fields of genomics and chromatin biology.

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