🧬 Smith-Waterman Sequence Alignment Suite (for now) 🧬

A comprehensive toolkit for DNA sequence alignment using the Smith-Waterman algorithm, featuring high-performance parallel implementation, interactive visualization, and real-time performance analysis.

📁 Project Structure

├── align/
│   ├── smith_waterman.go             # Sequential implementation
│   ├── parallel_smith_waterman.go    # Parallel implementation
│   ├── benchmark_test.go             # Performance tests
│   └── smith_waterman_test.go        # Test Suite for S_W
├── data/
│   ├── dna.go                        # DNA sequence utilities
│   └── dna_test.go                   # Testing utilities
├── cmd/
│   ├── benchmark/                    # Benchmarking tools
│   │   └── main.go
│   ├── profile/                      # Profiling tools
│   │   └── main.go
│   ├── visualize/                    # Visualization utilities
│   │   └── main.go
│   │ 
│   └── webui/                        # Web interface
│       ├── README.md
│       ├── main.go
│       ├── templates/
│       │   └── index.html
│       └── static/
│           ├── css/
│           │   └── styles.css
│           └── js/
│               └── main.js
└── README.md                         # Documentation

✨ Key Features

🚀 High-Performance Algorithms

• 💻 Sequential Smith-Waterman

  • Traditional single-threaded implementation
  • Baseline for performance comparison
  • Optimized matrix calculation and traceback

• ⚡ Parallel Smith-Waterman

  • Multi-threaded implementation using goroutines
  • Wave-front parallelization approach
  • Configurable worker count
  • Up to 5x speedup on large sequences

• 📦 Batch Processing

  • Concurrent alignment of multiple sequences
  • Efficient workload distribution
  • Perfect for genomic database searches

🔍 Analysis & Profiling

• 📊 Benchmarking Suite

  • Comprehensive performance testing
  • Comparison of sequential vs. parallel implementations
  • Memory usage analysis
  • Scalability testing across sequence lengths

• 🧰 Profiling Toolkit

  • CPU and memory profiling
  • Execution time measurement
  • Resource usage tracking
  • Performance bottleneck identification

🌐 Web Interface

• 📋 Sequence Input

  • Direct entry of DNA sequences
  • Random sequence generation
  • Batch sequence processing

• ⚙️ Execution Controls

  • Toggle between sequential and parallel algorithms
  • Configure worker count
  • Enable/disable batch processing

• 📈 Performance Monitoring

  • Real-time execution timing
  • Memory usage tracking
  • Interactive performance charts
  • Historical data comparison

• 🔬 Alignment Visualization

  • Interactive sequence viewer
  • Color-coded matches, mismatches, and gaps
  • Mutation detection and highlighting
  • Batch results comparison

🎨 Visualization Tools

• 🖼️ Static HTML Reports

  • Standalone alignment visualization
  • Mutation analysis and statistics
  • Shareable results

• 🌍 Interactive Web Visualizer

  • Browser-based alignment explorer
  • Realtime mutation detection
  • Sequence highlighting

🚀 Usage Examples

🖥️ Web UI

# Start the web interface
go run cmd/webui/main.go
# Access at http://localhost:8080

📊 Benchmarking

# Run benchmarks across different sequence lengths
go run cmd/benchmark/main.go --mode=all --lengths=100,500,1000,2000

🔍 Profiling

# Profile parallel execution
go run cmd/profile/main.go --mode=parallel --length=2000 --workers=4 --cpuprofile=cpu.prof

🎨 Visualization

# Generate visualization of an alignment
go run cmd/visualize/main.go --output=report.html --query=GATTACA --reference=GATCACA

# Start visualization server
go run cmd/visualize/main.go --server --port=8081 --random --length=1000

🧪 Testing

# Run all tests
go test ./...

# Run benchmarks
go test -bench=. ./align

🔧 Technical Details

• 🧬 Mutation Detection

  • Single Nucleotide Polymorphisms (SNPs)
  • Insertions and deletions (indels)
  • Complex mutation patterns

• ⚙️ Parallelization Strategy

  • Wave-front decomposition
  • Row/column partitioning
  • Optimized for multi-core processors

• 📉 Performance Characteristics

  • Parallel algorithm scales with sequence length
  • Optimal for sequences > 500 bp
  • Memory usage optimized for large datasets

🏆 Advantages

• ⏱️ Speed: Significant speedup through parallelization
• 📱 Usability: Intuitive web interface for non-technical users
• 📊 Analysis: Comprehensive performance insights
• 🧩 Flexibility: Command-line and web-based interfaces
• 🔧 Extensibility: Modular design for easy enhancement

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🧬 Smith-Waterman Sequence Alignment Suite - Making DNA analysis faster, easier, and more insightful.