Abstract
Bioinformatics, which is now a well-known field of study, originated in the context of biological sequence analysis. Now in bioinformatics, we found a wide range of applications in the domain of molecular biology, which focuses on the analysis of molecules. The most important thing in the domain of DNA sequence analysis is sequence analysis. Sequence analysis algorithms are designed to identify the origin of viruses, the amount of similarity between viruses/species, and also the amount of mutation occurs in the viruses/species. In this paper, we adopt a new approach in DNA sequence similarity analysis that differs from the attempts made in the past. Our method is highly effective, accurate, and reliable to identify the similarity with point mutation. Our goal is to reduce the time complexity of similarity analysis. We find the amount of similarity among different species. It takes \(\mathcal {O}(n)\) time to identify the amount of similarity with a point mutation, which is faster than the other alignment algorithms including the Needleman-Wunsch (NW) algorithm, FOGSAA, MEGA, and BLASTN.
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Mondal, P., Basuli, K. (2022). OAIPM: Optimal Algorithm to Identify Point Mutation Between DNA Sequences. In: Mandal, J.K., Buyya, R., De, D. (eds) Proceedings of International Conference on Advanced Computing Applications. Advances in Intelligent Systems and Computing, vol 1406. Springer, Singapore. https://doi.org/10.1007/978-981-16-5207-3_33
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DOI: https://doi.org/10.1007/978-981-16-5207-3_33
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