Ant Colony Optimization Techniques for Efficient Multiple Sequence Alignment

Multiple Sequence Alignment (MSA) is a fundamental task in bioinformatics, used to identify conserved regions and evolutionary relationships across biological sequences. However, as the number of sequences grows, the computational complexity of MSA increases significantly, making it challenging to handle large datasets efficiently.
To find conserved areas and evolutionary links between biological sequences, multiple sequence alignment, or MSA, is a basic bioinformatics technique. The computational cost of MSA, however, rises sharply with the number of sequences, making it difficult to manage big datasets effectively.
Rajeev Kumar PathakIt becomes more difficult to efficiently process large datasets as the number of sequences increases since MSA's computational cost grows exponentially. Through the use of Ant Colony Optimization (ACO) and the Divide and Conquer (D&C) method, this work introduces a novel algorithm that enhances the MSA process.