International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 9, No. 11, 8 Nov. 2017
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Aligning Molecular Sequences by Wavelet Transform using Cross Correlation Similarity Metric
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Author(s)
J.Jayapriya
1,*
Michael Arock
1
Index Terms
Sequence alignment, wavelet transform, cross-correlation, EIIP (Electron-Ion Interaction Potentials), PSM (Position Specific Matrix)
Abstract
The first fact of sequence analysis is sequence alignment for the study of structural and functional analysis of the molecular sequence. Owing to the increase in biological data, there is a trade-off between accuracy and the computation of sequence alignment process. Sequences can be aligned both in locally and globally to gives vital information for biologists. Focusing these issues, in this work the local and global alignment are focused on aligning multiple molecular sequences by applying a wavelet transform. Here, the sequence is converted into numerical values using the electron-ion interaction potential model. This is decomposed using a type of wavelet transform and the similarity between the sequences is found using the cross- correlation measure. The significance of the similarity is evaluated using two scoring function namely Position Specific Matrix and a new function called Count score. The work is compared with Fast Fourier Transform based approach and the result shows that the proposed method improves the alignment.
Cite This Paper
J.Jayapriya, Michael Arock, "Aligning Molecular Sequences by Wavelet Transform using Cross Correlation Similarity Metric", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.11, pp.62-70, 2017. DOI:10.5815/ijisa.2017.11.08
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