A Signal Processing Approach for Eucaryotic Gene Identification

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 249)


Signal processing has a great role in innovative developments in technology. Its techniques have been applied mostly in every field of science and engineering. In the field of bioinformatics it has to play an important role in the study of biomedical applications. Accurate prediction of protein coding regions (Exons) from genomic sequences is an increasing demand for bioinformatics research. Many progresses made in the identification of protein coding regions during the last few decades. But the performances of the identification methods still required to be improved. This paper deals with the identification of protein-coding regions of the DNA sequence mainly focus on analysis of the gene introns. Applications of signal processing tools like spectral analysis, digital filtering of DNA sequences are explored. It has been tried to develop a new method to predict protein coding regions based on the fact that most of exon sequences have a 3-base periodicity. The period-3 property found in exons helps signal processing based time-domain and frequency domain methods to predict these regions efficiently. Also, an efficient technique has been developed for the identification of protein coding region based on the period-3 behavior of codon sequences. It is based on time domain periodogram approach. Here it has been identified the protein coding regions, wherein we reduced the background noise significantly and improve the identification efficiency. In addition to this also comparison is done between time domain periodogram and the existing frequency based techniques. Simulation results obtained are shown the effectiveness of the proposed methods. This proves that the DSP techniques have important applications in obtaining useful information from these gene sequences.


Signal Processing Method DNA Exon Frequency domain methods GCF TDP 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Mount, D.W.: Bioinformatics: Genome and Sequence Analysis, 2nd edn. Cold Spring Harbor Laboratory Press, New York (2004)Google Scholar
  2. 2.
    Anastassiou, D.: Genomic signal processing. IEEE Signal Processing Magazine 18(4), 8–20 (2001)CrossRefGoogle Scholar
  3. 3.
    Anastassiou, D.: DSP in Genomics. In: Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, Salt lake City, Utah, USA, pp. 1053–1056 (May 2001)Google Scholar
  4. 4.
    Voss, R.F.: Evolution of long-range fractal correlations and 1/f noise in DNA base se-quences. Phy. Rev. Lett. 68(25), 3805–3808 (1992)CrossRefGoogle Scholar
  5. 5.
    Nair, A.S., Sreenathan, S.P.: A Coding measure scheme employing electron-ion interaction pseudo potential (EIIP). Bioinformation by Bioinformatics Publishing GroupGoogle Scholar
  6. 6.
    Ahmad, M., Abdullah, A., Buragga, K.: A Novel Optimized Approach for Gene Identification in DNA Sequences. Journal of Applied Sciences, 806–814 (2011)Google Scholar
  7. 7.
    Deng, S., Chen, Z., Ding, G., Li, Y.: Prediction of Protein Coding Regions by Combining Fourier and Wavelet transform. In: Proc. of the 3rd IEEE Int. Congress on Image and Signal Processing, pp. 4113–4117 (October 2010)Google Scholar
  8. 8.
    Akhtar, M., Epps, J., Ambikairajah, E.: Signal Processing in sequence analysis: Advances in Eukaryotic Gene Prediction. IEEE Journal of Selected Topics in Signal Processing 2(3), 310–321 (2008)CrossRefGoogle Scholar
  9. 9.
    Biju, V.G., Mydhili, P.: Genetic Algorithm Based indicator sequence method for exon prediction. In: Proc. of the IEEE int. Conf. on Advances in Computing, Control, & Telecommunication Technologies, pp. 856–858 (December 2009)Google Scholar
  10. 10.
    Voss, R.: Evolution of Long-Range Fractal Correlations and 1/f Noise in DNA Base Sequences. Physical Review Letters 68(25), 3805–3808 (1992)CrossRefGoogle Scholar
  11. 11.
    Hota, M.K., Srivastava, V.K.: Identification of Protein coding regions using Antinotch filters. Digital Signal Processing 22(6), 869–877 (2012)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Silverman, B.D., Linsker, R.: A Measure of DNA Periodicity. Journal of Theoretical Biology 118(3), 295–300 (1986)CrossRefGoogle Scholar
  13. 13.
    Tiwari, S., Ramachandran, S., Bhattacharya, A., Bhatta-charya, S., Ramaswamy, R.: Identification of Probable Genes by Fourier Analysis of Genomic Sequences. Bioinformatics 13(3), 263–270 (1997)CrossRefGoogle Scholar
  14. 14.
    Anastassiou, D.: Digital Signal Processing of Bio-molecular Sequences. Technical Report, Columbia University, 2000-20-041 (April 2000)Google Scholar
  15. 15.
    Anastassiou, D.: Frequency-Domain Analysis of Biomolecular Sequences. Bioinformatics 16(12), 1073–1082 (2000)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Fickett, J.W.: The gene identification problem: an overview for developers. Comput. Chem. 20, 103–118 (1996)CrossRefGoogle Scholar
  17. 17.
    Vaidyanathan, P.P., Yoon, B.J.: The role of signal processing concepts in genomics and proteomics. J. Franklin Inst. 341, 111–135 (2004)CrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.ITERSiksha ‘O’ Anusandhan UniversityBhubaneswarIndia

Personalised recommendations