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Sequence alignment is one of the most important tools available to molecular biologists in order to find similarities between two or more DNA or protein sequences. However, despite several advances in this field, multiple sequence alignment (MSA) is often a slow process, and still, optimum results cannot be guaranteed. MSA is an important prerequisite for constructing phylogenetic trees, which are often the underlying goal for biologists to trace the evolution of closely related species. In this paper, we propose a randomized algorithm that can produce a sufficiently good MSA, for huge datasets, in a fraction of the time taken by contemporary MSA algorithms.