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40120140502004
40120140502004
40120140502004
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40120140502004
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40120140502004

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  • 1. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – INTERNATIONAL JOURNAL OF ELECTRONICS AND 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 30-35 © IAEME COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Volume 5, Issue 2, February (2014), pp. 30-35 © IAEME: www.iaeme.com/ijecet.asp Journal Impact Factor (2014): 3.7215 (Calculated by GISI) www.jifactor.com IJECET ©IAEME GENERATION OF INDICATOR SEQUENCE USING TMS320VC5416 FIXED-POINT DIGITAL SIGNAL PROCESSOR M. Mohanty1, Dr. M. Basu2, Dr. D.N. Pattanayak3 1 Dept.of ETC, Gandhi Engineering College, Madanpur, Bhubaneswar, Odisha, India Dept.of Power Engg, Jadavpur University, Saltlake campus, Kolkata, West Bengal, India 3 Dept. of EEE, Trident Academy of Tchnology, Infocity Area. Patia, Bhubaneswar, Odisha, India 2 ABSTRACT The traditional as well as modern signal processing methods are playing an important role in molecular biology, which is related to the most commonly used terms i.e. DNA (Deoxyribose Nucleic Acid), gene & Protein. The last few years have seen the blooming of the genomic era, a period during which a wide range of research effort has been taken on the fractal behavior of DNA sequences which leads to the research in Genomic signal processing, primarily defined as the processing of genome sequence that contains the whole hereditary information of a living organism, encoded in the DNA. This paper concerns with a brief review of some fundamentals related to molecular biology followed by the implementation of Digital Signal Processor (DSP) for the processing of DNA sequence. The flexibility of the design & presence of on-chip memory in the DSP reduces the memory access time as compared to general purpose microprocessor or the microcontroller. Keywords: DNA, DSP, CCS. INTRODUCTION Recently, signal processing is booming as the most highlighted field approaching towards the detection, prediction and classification of genetic regulatory networks that are capable of modeling genetic behavior. It is a method of extracting information from the signal which, in turn, depends upon the type of signal and the nature of information it carries. Therefore, the signal processing is concerned with the representation, transformation, and manipulation of signals and the information they contain [1]. The Genomic Signal Processing (GSP) has been defined as the processing of genomic signals and its use for gaining biological knowledge and the translation of that knowledge into system based applications [11]. The aim of GSP is to integrate the theory and methods of signal 30
  • 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 30-35 © IAEME processing with the global understanding of functional genomics, with special emphasis on genomic regulation. In a very real sense, it must be specified that genomic information is digital in nature, is represented in the form of sequence. A DNA sequence is a double helical structure, has been mathematically represented by character string, which is made from an English alphabet of four elements, namely A, T, C, and G, shown in fig. 1. The letter A, T, C, G represents molecules called nucleotides or bases as shown in fig 1. ‘A’ is known as Adenine, ‘T’ as Thymine, ‘C’ as Cytosine and ‘G’ as Guanine. The main role of DNA is the long-term storage of information and for constructing other components of cells, such as proteins and RNA (Ribose Nucleic Acid) molecules, as it contains the essential instructions. Fig.1: Structure of DNA containing the nucleotides The example of a part of DNA sequence is shown in “(1)”. …..ATCGGATTCCAGTGAC… (1) The double helical structure of DNA straightened out for simplicity, demonstrating a simple schematic diagram for part of a DNA molecule as shown in fig. 2 [18-20]. The base ‘A’ of one strand is always pairs with ‘T’ of other strand and similarly ‘C’ of one strand always pairs with ‘G’ of other strand. Therefore the number of nucleotides ‘A’ and ‘T’ always same in a DNA sequence similarly for ‘G’ and ‘C’. This type of pairing is called the Watson and Crick base pairing. Therefore, the DNA sequence of each organism is represented by the number of base pairs. Since DNA contains the genetic information of living organisms, so life is governed by quaternary codes [18]. Fig.2: The DNA double helix (linearized schematic) A DNA sequence can be separated into two types of regions: genes and intergenic spaces. Again, gene has two types of sub regions called the exons and introns as shown in fig. 3. However, 31
  • 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 30-35 © IAEME genes carry the coding information for the generation of proteins. Basically exons region is responsible for that purpose. In this paper, an attempt has been made for the processing of a particular DNA sequence with the Texas Instrument TMS320VC5416 Digital Signal Processor (DSP)[12]. The special reason for selecting this platform is its advanced modified Harvard architecture having huge computational power along with the fast processing capability that facilitate the implementation process efficiently in comparison with the general purpose processor[8-10] and micro controllers[14]. TMS320VC5416 Digital Signal Processor is a 16 bit fixed point Complex Instruction Set Computer (CISC) processor. Mostly, in the commercial field, the 16-bit processor is used from the cost effective point of view. The modified Harvard architecture of the DSP provides the pipelining operation which is absent in the Von Neumann architecture of the general-purpose processors [12-14]. The pipelining property of the processor reduces the execution time of the instruction as more than one instruction is processed parallely. Therefore, the digital signal processor is mostly used for the real time application [2,5,12]. Fig.3: DNA sequence showing exons (protein coding region) and introns Another remarkable advantage of implementing the TMS320VC5416 digital signal processor is that, the processor facilitates quick implementation of algorithm with the help of Code Composer Studio (CCS) Integrated Development Environment (IDE) [15-17]. The modification of algorithm and design parameter need not require any change in the hardware as the digital signal processor is programmable. So just by reprogramming the source code, the modification can be done. TMS320VC5416 is a widely used processor for in lab research purpose which is also called Million Floating Point Processor (MFLOPS), makes it ideal candidate for this work. The choice of TMS320VC5416 processor has provided flexibility and has considerably saved time for experimental analysis. GENERATION OF INDICATOR SEQUENCE A single DNA strand is represented as a sequence of four nucleotides, namely Adenine (A), Cytosine (C), Thymine (T), and Guanine (G). For the application in the digital signal processing field it is necessary to convert the character string of the DNA sequence into a set of digital signals. Basically, the indicator sequence indicates the location of each nucleotide in the long DNA sequence. For example, Let the DNA sequence is ATCTAGCTACTAGG…………(1) So, the corresponding indicator sequence for the base A, represented as xA(n), in the binary form is, xA(n)=10001000100100…… (2) n→ length of the DNA sequence or it can be said the number of bases present in the DNA sequence. In the equation (1), ‘1’ indicates the presence of A and ‘0’ indicates its absence. The indicator sequences for the other bases are defined similarly. In this work, the original DNA sequence is broken down into four binary sequences of same length for four nucleotides, so that, the corresponding binary sequence of each nucleotide will indicate its 32
  • 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 30-35 © IAEME position in the original sequence. It is carried out on a particular organism i.e. Bovine gastrine gene of Bovgas having 1066 base pairs. FLOW CHART The flow chart explains the important steps of the program. It gives the brief idea about the logic for developing the program using TMS320VC5416 processor to generate the indicator sequence for each nucleotide. 33
  • 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 30-35 © IAEME EXPERIMENTAL RESULT After the successful downloading of the program into the program memory of the processor, an input window will be displayed on the screen, where we can give the input. Then the input will be where processed through the processor, so that, a Stdout tab is opened at the bottom of the window to show the corresponding output which is sent to stdout by the program. A part of the DNA sequence of Bovgas is taken for experimental purpose for the ease of checking on the screen, whose aken corresponding four indicator sequences are shown in the Fig. 4. Fig 4: The generation of indicator sequence for Bovgas In the program, the length is taken as a variable, so that we can take any length of sequence according to the requirement. CONCLUSION This paper eyes on making Genetic Studies easier in the hardware platform for the processing of DNA sequence. This successful hardware implementation of Generation of indicator sequence has essful opened the gates for research works in integrating them into Real-time applications. One aspect of Real time this work is to provide a method for identifying and to locate genes or genomic region of interest in regions the genome. Another aspect is to provide a method for identifying genetic associations and converting information into an appropriate data format so that it could be used in practical applications such as identification of disease disease-causing genes in complex diseases, in population population-based association study, a family-based association study, or a traditional linkage study. based REFERENCES [1] Sanjay Sharma, Digital Signal Processing, 6th ed., S.K.Kataria & Sons, Publishers of Engineering & Computer Books, New Delhi, India, 2010. 20 [2] B. Venkataramani, M.Bhaskar, Digital Signal Processors Architecture, Programming and Applications, Tata McGraw-Hill, New Delhi, India, 2010. Hill, 20 [3] Alan V. Oppenhum, Application of Digital Signal Processing, Prentice Hall, Inc, Eng Prentice-Hall, Englewood Cliffs, N.J., U.S.A, 1978. 34
  • 6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 30-35 © IAEME [4] Alan V. Oppenhum, Ronald W. Schafer, Digital Signal Processing, Prentice-Hall of India Private Limited, New Delhi, India, 1997. [5] Avtar Singh, Digital Signal Processing Implementation: Using DSP Microprocessors- with [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] examples from TMS320C54XX, Belmont, CA: Thomson Brooks/Cole, 2004. TMS320VC5416 Fixed-Point Digital Signal Processor: Data Manual, Literature Number: SPRS095J, Texas Instruments, March, 1999-Revised April, 2003. TMS320VC5416 Fixed-Point Digital Signal Processor: Data Manual, Literature Number: SPRS095J, Texas Instruments, March, 1999-Revised April, 2003. R.Gaonkar, Microprocessor Architecture, Programming and Application: with 8085/8085A, 5th ed., Prentice-Hall of India Private Limited, New Delhi, India, 2002. Anokh Singh and A.K.Chhabra, Fundamentals of Digital Electronics and Microprocessors, S.Chand & Company Limited, New Delhi, India, 2005. Barry B. Brey, The Intel Microprocessors, 8086/8088, 80186/80188, 80286, 80386, 80486, Pentium, Pentium Pro Processor, Pentium-II, Pentium-III, Pentium-4, Architecture, Programming and Interfacing, 8th ed., Prentice-Hall of India Pvt. Ltd., New Delhi, 2008. Edward R. Dougherty, Llya Shmulevich, Jie Chen, and Z. Jane Wang, Genomic Signal Processing and Statistics, Volume-2, Hindawi Publishing Corporation, New York, U.S.A, ISBN 977-5945-07-0, 2005. Avtar Singh and S.Srinivasan, Digital Signal Processing Implementations: Using DSP Microprocessors – with examples from TMS320C54x, Thomson Learning, Inc., 2011. M. Morris Mano, Computer System Architecture, 3rd ed., Prentice Hall of India Private Limited, 2007. Kenneth J. Alaya, The 8051 Microcontroller Architecture, Programming and Application, 3nd ed., Cengage Learing ,2000. TMS320C54x Code Composer Studio Tutorial, Texas Instruments, Texas, 1999. TMS320C54xx Optimizing C/C++ Compiler User’s Guide, Texas Instruments Literature Number SPRU103G, October, 2002. Code Composer Studio Getting Started Guide, Texas Instruments Literature Number SPRU509. P.P.Vaidyanathan, Genomics and Proteomics: A Signal Processor’s Tour, Vol-4, Number-4, IEEE Circuits and Systems Magazine, U.S.A., Fourth Quarter 2004. D.Anastassion, Genomic Signal Processing, IEEE Signal Processing Magazine, vol-18, no.4, pp 8-20, 2001. P.P.Vaidyanathan and Byung-Jun Yoon, The role of Signal Processing Concepts in Genomics and Proteomics, Journal of Franklin Institute, Special Issue on Genomics, 2004. Deep Kamal Kaur Randhawa, Inderpreet Kaur, Lalit M. Bharadwaj and M.L.Singh, “Study of Hlgs and Transfer Integrals of DNA Bases for Investigating Charge Conduction”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 2, Issue 1, 2011, pp. 43-49, ISSN Print: 0976 - 6464, ISSN Online: 0976 –6472. Vijay Arputharaj J and Dr.R.Manicka Chezian, “Data Mining with Human Genetics to Enhance Gene Based Algorithm and DNA Database Security”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 3, 2013, pp. 176 - 181, ISSN Print: 0976 - 6367, ISSN Online: 0976 – 6375. D. Pattanayak, M. Basu and R. N. Chakrabarti, “Multi-Objective Differential Evolution for Optimal Power Flow”, International Journal of Electrical Engineering & Technology (IJEET), Volume 3, Issue 1, 2012, pp. 31 - 43, ISSN Print: 0976 - 6545, ISSN Online: 0976-6553. 35

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