Sequence Alignment Tool

A GUI application implementing two fundamental bioinformatics algorithms for sequence alignment:

• Smith-Waterman (Local Alignment)
• Needleman-Wunsch (Global Alignment)

Features

• Intuitive graphical interface for entering DNA/protein sequences
• Adjustable scoring parameters (match, mismatch, gap penalties)
• Side-by-side alignment display
• Dynamic scoring matrix visualization
• Support for both local and global alignment strategies

Requirements

pip install numpy

Note: tkinter comes built-in with Python on most systems.

Usage

Run the Application

python sequence_alignment_gui.py

Input Parameters

Sequences: Enter DNA or protein sequences (case-insensitive)

• Sequence 1: First sequence to align
• Sequence 2: Second sequence to align

Scoring Parameters:

• Match Score: Points awarded for matching characters (default: 2)
• Mismatch Score: Points deducted for non-matching characters (default: -1)
• Gap Penalty: Points deducted for gap insertion (default: -1)

Running Alignments

1. Enter your sequences and parameters
2. Click "Run Smith-Waterman" for local alignment or "Run Needleman-Wunsch" for global alignment
3. View results and scoring matrix below

Algorithm Differences

Smith-Waterman (Local Alignment)

• Finds the best matching substring between two sequences
• Useful for finding conserved regions in sequences
• Always produces positive scores for high-scoring regions
• Gap penalties never force the algorithm to continue

Needleman-Wunsch (Global Alignment)

• Aligns entire sequences from start to end
• Useful when comparing complete sequences
• Requires initialization of first row and column
• Guarantees alignment of entire sequences with gaps as needed

Example

Try these sequences with default parameters:

Sequence 1: AGCTAG
Sequence 2: AGTAG

Expected Smith-Waterman result: Score = 9
Expected Needleman-Wunsch result: Score = 9

Files

• sequence_alignment_gui.py - Main GUI application (run this one)
• test_alignment.py - Algorithm test suite
• README.md - This documentation

Algorithm Complexity

• Time Complexity: O(m × n) where m and n are sequence lengths
• Space Complexity: O(m × n) for dynamic programming matrices

Notes

• Both algorithms use dynamic programming matrices
• The matrix display shows all intermediate scores
• Alignment visualization uses:
  • | for exact matches
  • * for mismatches
  • - for gaps

Customization

You can modify scoring parameters for specific use cases:

• DNA matching (ATGC): Default parameters work well
• Protein sequences: Adjust match/mismatch based on BLOSUM or PAM matrices
• Stricter/looser alignment: Adjust gap penalties

Quick Start

1. Open Command Prompt or Terminal
2. Navigate to the Sequence_Alignment_Project folder
3. Run: pip install numpy (first time only)
4. Run: python sequence_alignment_gui.py
5. Enter sequences and click buttons to run alignments