An enhanced algorithm for multiple sequence alignment of protein sequences using genetic algorithm

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Multiple sequence alignment (MSA) is crucial for understanding protein and DNA sequence relationships.
• Accurate MSA is essential for biological sequence analysis and evolutionary studies.

Purpose of the Study:

• To develop and evaluate a novel genetic algorithm for improving multiple sequence alignment (MSA).
• To assess the alignment quality and population evolution using genetic operators like crossover and mutation.

Main Methods:

• A genetic algorithm approach was designed for MSA.
• Key genetic operators, crossover and mutation, were implemented and analyzed.
• The method was tested using the BALIBASE protein benchmark dataset.

Main Results:

• The proposed genetic algorithm achieved higher alignment quality scores compared to existing methods.
• Performance was evaluated against established algorithms including SAGA, CLUSTALW, and DIALIGN.
• Experiments confirmed the algorithm's effectiveness across diverse datasets.

Conclusions:

• The developed genetic algorithm offers a significant improvement in MSA quality.
• This method provides a more accurate approach for aligning biological sequences.
• The findings suggest potential for enhanced biological sequence analysis through this algorithm.