🧬 Parallel Gene Sequence Alignment (Needleman-Wunsch & MPI)

This repository contains a high-performance implementation of the Needleman-Wunsch algorithm for global sequence alignment. Using MPI (Message Passing Interface), the system parallelizes the comparison of multiple genetic sequences, specifically focusing on the COX1 (Cytochrome c oxidase subunit I) gene across different species.

🚀 Key Features

• Parallel Computing with MPI: Distributes alignment tasks across multiple CPU cores using mpi4py, significantly reducing the time complexity for large-scale comparisons.
• Global Alignment Algorithm: A robust dynamic programming implementation of the Needleman-Wunsch algorithm for optimal similarity scoring.
• Bioinformatics Integration: Seamlessly parses FASTA records using Biopython for systematic genomic analysis.
• Scalable Architecture: Designed to scale linearly with the number of available processors.

🛠 Tech Stack

• Programming Language: Python
• Parallelism: mpi4py (MPI for Python)
• Library Support: Biopython (SeqIO), NumPy
• Dataset: 16 distinct COX1 gene sequences retrieved from GenBank (FASTA format).

📊 How It Works

1. Master Node (Rank 0): Loads 16 COX1 sequence files from the /data directory.
2. Broadcast: Distributes sequence data to all worker nodes via comm.bcast.
3. Parallel Tasking: Worker nodes independently calculate alignment scores for assigned pairs.
4. Gather: Rank 0 collects all scores and outputs the final evolutionary distance matrix/summary.

📂 Project Structure

• parallel_alignment.py: Core parallelized Python script.
• /data: Contains 16 sample .fasta files for immediate testing.
• requirements.txt: Project dependencies.

🏃 Installation & Usage

1. Requirements

Ensure you have an MPI implementation (like MS-MPI or MPICH) installed on your system.

pip install mpi4py biopython numpy