Computational Study and Performance Evaluation of Different Genomic Signal Processing Methods for Identification of Protein Coding Regions (Exon Regions) of DNA Sequence

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 85 - Number 11
Year of Publication: 2014
Authors:
M. N. Vamsi Thalatam
Allam Appa Rao
10.5120/14884-3318

Vamsi M N Thalatam and Allam Appa Rao. Article: Computational Study and Performance Evaluation of Different Genomic Signal Processing Methods for Identification of Protein Coding Regions (Exon Regions) of DNA Sequence. International Journal of Computer Applications 85(11):11-15, January 2014. Full text available. BibTeX

@article{key:article,
	author = {M. N. Vamsi Thalatam and Allam Appa Rao},
	title = {Article: Computational Study and Performance Evaluation of Different Genomic Signal Processing Methods for Identification of Protein Coding Regions (Exon Regions) of DNA Sequence},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {85},
	number = {11},
	pages = {11-15},
	month = {January},
	note = {Full text available}
}

Abstract

Recent developments in the area of genomic signal processing (GSP) reveal that this approach has important role in the analysis of genomic sequence, structure and function as well as the gene regulation of different organisms. In this paper we analyze different genomic signal processing methods used for identification of exon coding regions in DNA sequence. The gene sequences of interest are mapped to electron ion interaction potential (EIIP) values of nucleotides and these transformed numerical gene sequences are processed through different signal processing techniques like discrete Fourier transform (DFT), auto regressive (AR) and adaptive auto regressive (AAR) methods. The performance evaluation in terms of computational time is estimated and analyzed. By applying the EIIP mapped sequence to these DFT, AR and AAR methods, the effective computational time is abruptly reduced in AAR method compared to the DFT and AR methods. We tested five sequences of c-elegans) [AF099922], [FO080874. 2], [FO081434]), fruitfly [NM_170135] & homosapien (BDNF [NG_011794]).

References

  • Roy, M. , S. Biswas and S. Barman, 2009. Identification and Analysis of Coding and Noncoding Regions of a DNA Sequence by Positional Frequency Distribution of Nucleotides (PFDN) Algorithm, 4th International Conference on Computers and Devices for Communication, pp: 1-4.
  • Akhtar, M. , E. Ambikairajah and J. Epps, 2008. Advances in Eukaryotic Gene Prediction, IEEE Journal of Signal Processing in Sequence Analysis, Selected Topics in Signal Processing, 2(3): 310-321.
  • Voss RF. 1992. Evolution of long-range fractal correlations and 1/f noise in DNA base sequence. Physical Review Letters 68: 3805-3808.
  • I. Cosic, IEEE Trans Biomed Eng. , 41:12 (1994), [PMID: 7851912]
  • Hota, M. K. and V. K. Srivastava, 2008. DSP technique for gene and exon prediction taking complex Indicator sequence, IEEE Region 10 Conference (TENCON) pp: 1-6.
  • Achuthsankar S. Nair, Sivarama Pillai Sreenadhan A coding measure scheme employing electron-ion interaction pseudopotential (EIIP), ISSN 0973-2063,2006, Bioinformation.
  • Rao N, Shepherd SJ: Detection of 3-periodicity for small genomic sequence based on AR techniques. In Int. Conf. On comm. , IRC. And Sys, Volume 2 2004:1032–1036.
  • Akhatar M, Ambikairajah E, Epps J: Detection of Period-3 behavior in genomic sequence using singular value decomposition. In IEEE Int. Conf. On Emerging Technologies 2005:13–17.
  • Sitanshu Sekhar Sahu, Ganapati Panda, An efficient signal processing approach in eukaryotic gene prediction. Vol. 1-2010/Iss. 2, pp. 75-79, IJSIP
  • National Center for Biotechnology Information, website address available at: http://www. ncbi. nlm. nih. gov/.
  • V. Veljkovic & I. Slavic, (1972) "Simple General-Model Pseudopotential", Physical ReviewLetters, Vol. 29, No. 2, pp 105-107.
  • S. Tiwari et al. , "Prediction of probable genes by Fourier analysis of genomic sequences," CABIOS, vol. 13, no. 3, 1997. [PMID: 9183531]
  • J. Pardey,S. Roberts,L. Tarassenko, "A review of parametric modeling techniques for EEG analysis. University of Oxford, Oxford.
  • Richard E. Blahut, "Fast Algorithms for Signal Processing",pp:10-17,Cambridge University Press.
  • Bianchi A. , Mainardi L. , Meloni C. , Chierchia S. , Cerutti S. Continuous monitoring of the Sympatho-Vagal Balance through spectral analysis. IEEE Engineering in Medicine and Biology. 16(5): 64-73, 1997.
  • Pfurtscheller G, Neuper C, Schlögl A, Lugger K. Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Trans Rehabil Eng. 6(3):316-25, 1998.
  • A. S. Nair & T. Mahalakshmi, In Silico Biology, 6: 0019 (2006) [PMID: 16922684]
  • Haykin S. Adaptive Filter Theory. Prentice Hall, Englewood Cliffs, NJ, 1996.
  • T. K. Attwood and D. J. Parry-Smith, An Introduction to Bioinformatics, Addison Wesley Longman
  • David W. Mount, Introduction to Bioinformatics, Cold Spring Harbor Press.
  • Vinay K. Ingle , John G. Proakis, Digital Signal Processing Using MATLAB.