MSA A-Star / PA-Star Flask Application

A web-based interface for running Multiple Sequence Alignment (MSA) using A-Star and PA-Star algorithms.

Overview

This Flask application provides a user-friendly web interface for executing Multiple Sequence Alignment using optimized A-Star and Parallel A-Star (PA-Star) algorithms. It supports various benchmark sequence datasets and provides tools for managing sequences, binaries, and alignment results.

Features

• Multiple Algorithm Support: Run MSA using A-Star or PA-Star implementations
• Benchmark Datasets: Built-in support for BALIBASE, Benchmark, NUC, and PAM250 sequence datasets
• Binary Management: Easy selection and management of different algorithm binaries
• Sequence Management: Browse and select FASTA sequences from organized datasets
• Results Tracking: View and manage alignment execution results
• Parallel Processing: Support for multi-threaded execution with configurable thread count
• Cost Matrix Options: Support for different substitution matrices (PAM250, etc.)
• Web Interface: Clean, intuitive web UI for all operations

Project Structure

msa_app/
├── app.py                  # Application entry point
├── requirements.txt        # Python dependencies
├── app/
│   ├── __init__.py        # Flask app factory
│   ├── config.py          # Configuration settings
│   ├── routes/            # API routes
│   │   ├── binaries.py    # Binary management endpoints
│   │   ├── results.py     # Results viewing endpoints
│   │   ├── run.py         # Alignment execution endpoints
│   │   └── sequences.py   # Sequence browsing endpoints
│   ├── services/          # Business logic
│   │   ├── binaries_service.py
│   │   ├── results_service.py
│   │   ├── runner_service.py
│   │   └── sequences_service.py
│   ├── static/            # CSS and static assets
│   │   └── style.css
│   ├── templates/         # HTML templates
│   │   └── index.html
│   └── utils/             # Utility functions
│       ├── fasta.py       # FASTA file parsing
│       ├── filesystem.py  # File system operations
│       └── security.py    # Security utilities
├── bin/                   # Algorithm binaries (not in repo)
├── seqs/                  # Sequence datasets (not in repo)
│   ├── Balibase/
│   ├── Benchmark/
│   ├── NUC/
│   └── PROT/
└── results/               # Alignment results (generated)

Requirements

• Python 3.10+
• Flask
• Werkzeug

Installation

1. Clone the repository

   git clone https://github.com/vncsmnl/msa_app
   cd msa_app

2. Create a virtual environment (recommended)

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Set up directories

   Create the following directories if they don't exist:

   mkdir bin seqs results

5. Add algorithm binaries

   Place your A-Star and PA-Star executable binaries in the bin/ directory.

6. Structure

• seqs/Benchmark/ - Benchmark test sequences
• seqs/Balibase/ - BAliBASE benchmark suite
• seqs/NUC/ - Nucleotide sequences (DNA/RNA)
• seqs/PAM/ - Protein sequences organized by length

These sequences are used for:

• Testing the MSA algorithms
• Benchmarking performance
• Validating alignment quality

References

• BAliBASE: Benchmark Alignment Database

Usage

1. Start the server

   python app.py

2. Access the application

   Open your browser and navigate to:

   http://localhost:5000
   

3. Run an alignment

   • Select an algorithm (A-Star or PA-Star)
   • Choose a binary version
   • Browse and select a FASTA sequence file
   • Configure parameters (cost type, thread count)
   • Click "Run" to execute the alignment

4. View results

   Results are automatically saved and can be viewed through the results interface.

API Endpoints

Sequences

• GET /api/sequences/sources - List available sequence sources
• GET /api/sequences/<source> - Browse sequences in a source
• GET /api/sequences/<source>/file - Get sequence file content

Binaries

• GET /api/binaries - List available algorithm binaries

Execution

• POST /api/run - Execute MSA alignment
  • Parameters: algorithm, binary_name, file_path, cost_type, num_threads, verbose

Results

• GET /api/results - List all alignment results
• GET /api/results/<result_id> - Get specific result details

Configuration

Edit app/config.py to customize:

• Directory paths
• Sequence sources
• Server settings
• Secret key

License

MIT License

Notes

• The application runs with debug mode enabled by default
• Default server port is 5000
• Results are stored locally in the results/ directory
• Execution timeout is set to 10 minutes per alignment