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Parallelized Hierarchical Expected Matching Probability for Multiple Sequence Alignment

Kedhar M, DR. M. Rajasekhara Babu, Mayank G, Abhinivesh M
Department of Computer Science, VIT University, Vellore
Journal of Theoretical and Applied Information Technology, Vol. 64, No. 2, 2014

@article{m2014parallelized,

   title={Parallelized Hierarchical Expected Matching Probability for Multiple Sequence Alignment},

   author={M, Kedhar and Babu, DR. M. Rajasekhara and G, Mayank and M, Abhinivesh},

   year={2014}

}

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Sequence alignment of two or more than two biological sequences such as protein, DNA (Deoxyribonucleic acid) or RNA (Ribonucleic acid) is called MSA (Multiple Sequence Alignment). Sequence homology can be inferred from the resulting MSA. Existing System uses dynamic programming technique which suffers from exponential growth of time as the sequence grows. A Hierarchical Expected Matching Probability (HEP) Matrix scoring technique improves accuracy but it consumes more time. This paper presents an implementation of HEP on GPU’s to speed up the Multiple Sequence Alignment (MSA). The experiment results shown that there is 25% accuracy of MSA and also 4% of speedup.
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