P.K. Meher

Work place: Institute for Infocomm Research, Singapore

E-mail: pkmeher@i2r.star.edu.sg


Research Interests: Quantum Computing Theory, Cellular Automata, Automata Theory, Processor Design, Computer systems and computational processes


Pramod Kumar Meher has received PhD in Science from Sambalpur University, India in 1996. Currently, he is Senior Scientist in the Department of Embedded Systems, Institute for Infocomm Research, Singapore. Previously, he has worked as a Visiting Faculty in the School of Computer Engineering, Nanyang Technological University, Singapore. The main area of his research interest is design of dedicated and reconfigurable architectures for computation-intensive algorithms pertaining to signal processing, image processing, communication, intelligent computing and bioinformatics. Recently, he is tending his research towards more fundamental aspects of hardware design including the quantum dot cellular automata, and nanocircuits and systems.

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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