Bioinformatics Alignment Visualizer

A web-based tool for visualizing sequence alignment algorithms (Needleman-Wunsch and Smith-Waterman).

Features

• Global Alignment (Needleman-Wunsch algorithm)
• Local Alignment (Smith-Waterman algorithm)
• Interactive web interface
• Matrix visualization with traceback path highlighting
• Alignment score calculation

Requirements

• Python 3.x
• Flask

Installation

On Aurora/Fedora (using toolbox)

# Create and enter toolbox
toolbox create bioinformatics
toolbox enter bioinformatics

# Install Flask
sudo dnf install python3-flask

# Navigate to project directory
cd /var/home/merdan/Desktop/bioinformatics/bioinformatics-allignment-visualizer

# Run the application
python3 app.py

On other systems

# Install dependencies
pip install -r requirements.txt

# Run the application
python3 app.py

Usage

1. Start the application:

   python3 app.py

2. Open your web browser and navigate to:

   http://127.0.0.1:5000
   

3. Enter your sequences in the input fields

4. Select the alignment algorithm:

   • Global (Needleman-Wunsch): Aligns entire sequences
   • Local (Smith-Waterman): Finds best local alignment

5. Click Calculate Alignment to see results

Project Structure

bioinformatics-allignment-visualizer/
├── app.py                    # Application entry point
├── requirements.txt          # Python dependencies
├── test_alignment.py         # Test script for core algorithms
├── core/                     # Core alignment algorithms
│   ├── __init__.py
│   ├── alignment_global.py  # Needleman-Wunsch implementation
│   ├── alignment_local.py   # Smith-Waterman implementation
│   └── scoring.py           # Scoring functions
├── gui/                      # Web interface
│   ├── __init__.py
│   ├── web_server.py        # Flask server
│   └── templates/
│       └── index.html       # Web UI
└── utils/                    # Utility functions
    ├── __init__.py
    └── matrix_ops.py

Testing

Run the test suite to verify core algorithms:

python3 test_alignment.py

Algorithms

Global Alignment (Needleman-Wunsch)

• Aligns complete sequences from end to end
• Optimal for sequences of similar length
• Uses dynamic programming to find optimal global alignment

Local Alignment (Smith-Waterman)

• Finds best matching subsequences
• Optimal for finding conserved regions
• Uses dynamic programming with 0 as minimum score

Scoring

Default scoring parameters:

• Match: +1
• Mismatch: -1
• Gap: -2

These can be modified in core/scoring.py.