Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm.

Area of Science:

• Computational biology
• Bioinformatics
• Evolutionary biology

Background:

• Multiple sequence alignment (MSA) is fundamental for analyzing nucleotide and amino acid sequences.
• Accurate MSA is computationally challenging, impacting the discovery of functional, evolutionary, and structural relationships.
• Existing optimization algorithms often struggle to balance multiple alignment objectives effectively.

Purpose of the Study:

• To develop and evaluate a novel Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) for enhanced biological sequence alignment.
• To address the limitations of existing algorithms in achieving optimal accuracy and identifying conserved blocks.
• To compare the performance of MO-BFO against a wide range of established MSA methods.

Main Methods:

• Implementation of a Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO).
• Optimization objectives included maximizing similarity and non-gap percentage, and minimizing gap penalty.
• Validation using the BAliBASE 3.0 benchmark database.
• Comparative analysis against methods like Clustal Omega, Kalign, MUSCLE, MAFFT, GA, ACO, ABC, PSO, and GA-ABC.

Main Results:

• The proposed MO-BFO algorithm successfully identified conserved blocks, a limitation in previous hybrid approaches.
• MO-BFO demonstrated superior alignment accuracy compared to most widely used MSA methods.
• The algorithm achieved a non-dominated optimal solution by balancing multiple alignment objectives.

Conclusions:

• The Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) offers a significant advancement in multiple sequence alignment.
• MO-BFO provides a robust framework for improving the accuracy and biological relevance of sequence alignments.
• This approach enhances the discovery of functional and evolutionary insights from biological sequence data.