J.K. Meher

Work place: Computer Science and Engineering, Vikash College of Engineering for Women, Bargarh, Odisha, India.

E-mail: jk_meher@yahoo.co.in


Research Interests: Numerical Analysis, Mathematical Analysis


Jayakishan Meher has received his PhD from Sambalpur University, M.Tech in Electronics and Telecommunication Engineering from VSSUT, Burla (formerly known as University College of Engineering, Burla, Odisha, India) and M.Tech in Computer Science & Engg from RV University, India in 2012, 2002 and 2007 respectively. Currently he is Associate Professor and Head of the department of Computer Science and Engg in Vikash College of Engg for Women, Bargarh, Odisha, India. His research interests include digital signal processing, genome analysis, microarray data analysis, Protein analysis, metal binding, drug design and disease classification and other bioinformatics applications. Recently, he has developed interest in VLSI design for implementation of signal-processing algorithms on bioinformatics applications.

Author Articles
Wavelet Based Lossless DNA Sequence Compression for Faster Detection of Eukaryotic Protein Coding Regions

By J.K. Meher M.R. Panigrahi G.N. Dash P.K. Meher

DOI: https://doi.org/10.5815/ijigsp.2012.07.05, Pub. Date: 28 Jul. 2012

Discrimination of protein coding regions called exons from noncoding regions called introns or junk DNA in eukaryotic cell is a computationally intensive task. But the dimension of the DNA string is huge; hence it requires large computation time. Further the DNA sequences are inherently random and have vast redundancy, hidden regularities, long repeats and complementary palindromes and therefore cannot be compressed efficiently. The objective of this study is to present an integrated signal processing algorithm that considerably reduces the computational load by compressing the DNA sequence effectively and aids the problem of searching for coding regions in DNA sequences. The presented algorithm is based on the Discrete Wavelet Transform (DWT), a very fast and effective method used for data compression and followed by comb filter for effective prediction of protein coding period-3 regions in DNA sequences. This algorithm is validated using standard dataset such as HMR195, Burset and Guigo and KEGG.

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