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Polyphase filtering with variable mapping rule in protein coding region prediction

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Abstract

Genomic researches are concerned with the study of genomes of organisms. It has become a challenge to the researchers to identify the segments within the DNA sequence that involved in protein synthesis and called coding region of gene. The methods are generally used to identify the segment that relies on period-3 property of genes. This period-3 property easily can be identified by digital signal processing with great accuracy. Prior to DSP application in gene prediction a conversion rule is required which converts symbolic DNA (ATCGTC…) sequence into numerical representations. Accuracy of gene prediction depends on mapping rule. The effectiveness of mapping rule depends on the application area of genomics. Some mapping rule works well in gene prediction may not performed good in genetic disease prediction. Most of the available conversion rules are fixed mapping technique. In this paper a new conversion rule is proposed prior to DSP application and a polyphase filter is used to suppress the noise in the DNA spectrum. The performance of the proposed mapping is compared with existing mapping and also the performance of the polyphase filtering method is compared with existing filtering methods in terms of signal to noise ratio (SNR) and location accuracy.

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Abbreviations

DSP:

Digital signal processing

DNA:

Deoxyribo nucleic acid

mRNA:

Messenger ribo nucleic acid

PSD:

Power spectral density

DFT:

Discrete Fourier transform

IIR:

Infinite impulse response

FIR:

Finite impulse response

LPF:

Low pass filter

BPF:

Band pass filter

SNR:

Signal to noise ratio

References

  • Abo-Zahhad M, Ahmed SM, Abd-Elrahman SA (2012) Genomic analysis and classification of exon and intron sequences using DNA numerical mapping techniques. Int J Inf Technol Comput Sci 4(8):22–36

    Google Scholar 

  • Akhtar M, Epps J and Ambikairajah E (2007) On DNA numerical representations for period-3 based exon prediction. In: Proceedings of IEEE workshop on genomic signal processing and statistics (GENSIPS), pp. 1–4

  • Akhtar M, Epps J, Ambikairajah E (2008) Signal processing in sequence analysis: advances in eukaryotic gene prediction. IEEE J Sel Topics Signal Process 2(3):310–321

    Article  Google Scholar 

  • Alberts B, Bray D, Johnson A, Lewis J, Raff M, Roberts K, Walter P (1998) Essential cell biology. Garland Publishing Inc., New York

    Google Scholar 

  • Anastassiou D (2000) Frequency–domain analysis of bimolecular sequences. Bioinformatics 16:1073–1081

    Article  Google Scholar 

  • Anastassiou D (2001a) DSP in genomics: Processing and frequency domain analysis of character strings. IEEE-7803-7041-2001

  • Anastassiou D (2001b) Genomic signal processing. IEEE Signal Process Mag 18:8–20

    Article  Google Scholar 

  • Barman (Mandal) S, Biswas S, Das S and Roy M (2012) Performance analysis and Simulation of IIR anti-notch filter with various structures for gene predication application. 5th International Conference on Computer and Devices for Communication (CODEC)

  • Bellanger M, Bonnerot G, Coudreuse M (1976) Digital filtering by polyphase network: application to sample rate alteration and filter banks. IEEE Trans. Acoustic Speech Signal Proc. 24:109–114

    Article  Google Scholar 

  • Chakravarthy N, Spanias A, Iasemidis LD, Tsakalis K (2004) Autoregressive modeling and feature analysis of DNA sequences. EURASIP J Appl Sig Process 1:13–28

    Article  MATH  Google Scholar 

  • Crick FH, Watson JD (1953) Molecular structure of nucleic acids. Nature 171(4356):737–738

    Article  Google Scholar 

  • Cristea P D (2002a) Genetic signal representation and analysis. In: Proceedings of SPIEConference. International Biomedical Optics Symposium (BIOS’02), vol. 4623, pp 77–84

  • CristeaP D (2002) Conversion of nucleotides sequences into genomic signals. J Cell Mol Med 6:279–303

    Article  Google Scholar 

  • Epps J, Ambikairajah E and Akhtar M (2008) An integer period DFT for biological sequence processing. In: Proceedings of the IEEE International Workshop on Genomic Signal Processing and Statistics GENSIPS, pp 1–4

  • Ficket JW, Tung CS (1982) Recognition of protein coding regions in DNA sequences. Nucleic Acids Res 10(17):5303–5318

    Article  Google Scholar 

  • Grandhi D G and Vijaykumar C (2007) 2-Simplex Mapping for Identifying the Protein Coding Regions in DNA [C]. TENCON-2007, Taiwan, 530

  • Holden T, Subramaniam R, Sullivan R, Cheng E, Sneider C, Tremberger G, Flamholz JA, Leiberman DH, Cheung TD (1992) A TCG nucleotide fluctuation of Deinococcusradiodurans radiation genes. Proceedings of Society of Photo-Optical Nature, San Diego 168

    Google Scholar 

  • Inbamalar T M and Sivakumar R (2015) Improved Algorithm for Analysis of DNA Sequences Using Multiresolution Transformation. Scientific World J

  • Kakumani R and Devabhaktuni V (2008) Prediction of Protein Coding Regions in DNA Sequence using a model based approach. IEEE explore. doi:978-1-4244-1684-4/08, pp 1918–1920

  • Liu G and Luan Y (2014) Identification of protein coding regions in the eukaryotic DNA sequences based on Marple algorithm and wavelet packets transform. In Abstract and Applied Analysis (Vol. 2014). Hindawi Publishing Corporation

  • National Centre for Biotechnology Information (NCBI). http://www.ncbi.nlm.nih. Accessed on 28 June 2015

  • Ning J, Moore C N and Nelson J C (2003) Preliminary wavelet analysis of genomic sequences. Proc. IEEE Bioinformatics Conf. (CSB), pp 509–510

  • Rao N, Shepherd SJ (2004) Detection of 3-periodicity for small genomic sequences based on AR technique. Proc Int Conf Commun Circuits Syst ICCCAS 2:1032–1036

    Google Scholar 

  • Roy M, Biswas S and Barman (Mandal) S (2009) Identification and analysis of coding and non-coding regions of a DNA sequence by Positional Frequency Distribution of Nucleotides (PFDN) algorithm. International Conference on Computers and Devices for Communication (CODEC)

  • Sahu, S S and Panda G (2010) An efficient signal processing approach in eukaryotic gene prediction. In: Proceeding of 8th Asia Pacific Bioinformatic Conference (APBC), Bangalore, pp 1–12

  • Silverman BD, Linker R (1986) A measure of DNA periodicity [J]. Theor Biol 118:295–300

    Article  Google Scholar 

  • Singha Roy S, Barman S (2014) Identification of protein coding region of DNA sequence using multirate filter. Computational Advan Commun Circuits Syst. doi:10.1007/978-81-322-2274-3_16 (Lecture Notes in Electrical Engineering)

    Google Scholar 

  • Tiwari S, Ramachandran S, Bhattacharya A, Bhattacharya S, Ramaswamy R (1997) Prediction of probable genes by Fourier analysis of genomic sequences. CABIOS 3(3):263–270

    Google Scholar 

  • Vaidyanathan PP (1990) Multirate digital filters filter banks, polyphase networks, and applications: a tutorial. Proc IEEE 78(1):56–93

    Article  Google Scholar 

  • Vaidyanathan PP (2004) Genomics and proteomics: a signal processor’s tour. Circuits Syst Mag IEEE 4(4):6–29

    Article  Google Scholar 

  • Vaidyanathan P P and Yoon B J (2004) The role of signal-processing concepts in genomics and proteomics. J Franklin Inst (Special issue on Genomics)

  • Voss RF (1992) Evolution of long-range fractal correlations and 1/f noise in DNA base sequences. Phys Rev Lett 68(25):3805–3808

    Article  Google Scholar 

  • Yin C, Stephen S, Yau T (2007) Prediction of protein coding regions by the 3-base periodicity analysis of a DNA sequence. J Theor Biol 247:687–694

    Article  MathSciNet  Google Scholar 

  • Zhang R, Zhang CT (1004) Z curves, An Intuitive Tool, for Visualizing and Analyzing the DNA sequences. J Biomol Struct Dyn 11:767–782

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, 700009, India

    Saikat Singha Roy & Soma Barman

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  1. Saikat Singha Roy
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  2. Soma Barman
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Correspondence to Soma Barman.

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Singha Roy, S., Barman, S. Polyphase filtering with variable mapping rule in protein coding region prediction. Microsyst Technol 23, 4111–4121 (2017). https://doi.org/10.1007/s00542-016-2884-5

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