Bacterial Foraging Optimization -Genetic Algorithm for Multiple Sequence Alignment with Multi-Objectives.

Area of Science:

• Bioinformatics
• Computational Biology
• Evolutionary Computation

Background:

• Multiple Sequence Alignment (MSA) is computationally challenging for diverse protein sequences.
• Existing MSA algorithms face limitations in optimizing multiple objectives simultaneously.

Purpose of the Study:

• To develop a hybrid Bacterial Foraging Optimization-Genetic Algorithm (BFO-GA) for enhanced multiple sequence alignment.
• To improve multi-objective optimization in MSA, including gap penalty minimization and similarity maximization.

Main Methods:

• Implemented a hybrid BFO-GA algorithm incorporating variable gap penalties, similarity maximization, and non-gap percentage.
• Evaluated BFO-GA against established MSA methods (T-Coffee, Clustal Omega, MAFFT, etc.) on benchmark datasets (BAliBASE, Prefab, SABmark, Oxbench).
• Assessed alignment practicability by predicting phylogenetic trees using ClustalW2 and measuring Robinson-Foulds (RF) distance; statistical significance tested with Wilcoxon Signed-Rank test.

Main Results:

• The proposed BFO-GA algorithm demonstrated superior statistical significance in MSA compared to other well-known methods.
• BFO-GA achieved better results in phylogenetic tree prediction accuracy, as indicated by the Robinson-Foulds distance metric.
• The hybrid algorithm effectively optimized multiple MSA objectives, outperforming individual algorithms like GA, ACO, ABC, and PSO.

Conclusions:

• The BFO-GA algorithm offers a robust and effective approach for improving multiple sequence alignment accuracy.
• This method enhances the reliability of phylogenetic tree reconstruction based on sequence alignments.
• The BFO-GA presents a significant advancement in computational biology for analyzing protein sequence data.