Enhanced Single Bit DNA Squeezer (ESBDNAS)

Authors(2) :-Dr. S. Panneer Arokiaraj, Alam Jahaan

DNA Sequences contain concatenation of the four nucleotides or bases namely, Adenine, Guanine, Thymine and Cytosine. These nucleotides form a double stranded helix with each base of one strand joined to its complement on the other strand by using hydrogen bonds through base pairing rules. DNA compression may be carried out by reducing redundancy and exploiting the properties of DNA sequences. This paper proposes ENHANCED SINGLE BIT DNA SQUEEZER (ESBDNAS) an enhanced bit based lossless compression algorithm to compress a DNA sequence which implements two stages and is similar to SBDNAS (Single Bit DNA Squeezer) Algorithm. The proposed method converts the bases to bits 0 or 1 using the BDNAS algorithm and exploits the properties of DNA sequences that are inherent in it by substituting exact repeats, palindromes and their respective reverses for selected sub-sequences. This method achieves a compression ratio that is better than the existing lossless bit based DNA sequence compression algorithms.

Authors and Affiliations

Dr. S. Panneer Arokiaraj
Associate Professor, Computer Science, PERIYAR EVR College, Trichy, Tamil Nadu, India
Alam Jahaan
Research Scholar, Computer Science, PERIYAR EVR College, Trichy, Tamil Nadu, India

DNA sequence, bit based method, DNA compression, Bit DNA Squeezer, SBDNAS, Bit- based DNA compression

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Publication Details

Published in : Volume 3 | Issue 7 | September-October 2018
Date of Publication : 2018-10-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 345-351
Manuscript Number : CSEIT183760
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Dr. S. Panneer Arokiaraj, Alam Jahaan , "Enhanced Single Bit DNA Squeezer (ESBDNAS)", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 7, pp.345-351, September-October-2018.
Journal URL : http://ijsrcseit.com/CSEIT183760

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