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A New RFID and Cellular Automata Based Genetic Sequence Converter

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Future Information Technology - II

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 329))

Abstract

With the development of computer technology, the more information is obtained from biological experiments through computer analysis, and even has helped give rise to a new kind of science called bioinformatics. The commonly used tool in bioinformatics is sequence alignment. Sequence alignment is a way of comparing the sequences to identify regions of similarity that may be a consequence of functional relationships between the sequences. The genetic code is highly similar among all organisms and can be expressed in a simple table with 64 entries. In this research, we uses cellular automata (CA) theory as the research topic instead of using traditional dotplot or dynamic programming to conduct sequence alignment. The parallel computing characteristic of cellular automata makes the future expansion model tremendously decrease the massive sequence computing costs. This research modifies the originally defined rules of cellular automata in order to make it more appropriate for amino acid sequence alignment.

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References

  1. Adams MD, Kelley JM, Gocayne JD, Dubnick M, Polymeropoulos MH, Xiao H, Merril CR, Wu A, Olde B, Moreno RF et al (1991) Complementary DNA sequencing: expressed sequence tags and human genome project. Science 252(5013):1651–1656

    Google Scholar 

  2. Alston M, Johnson CG, Robinson G (2003) Colour merging for the visualization of biomolecular sequence data. In: Seventh International Conference on Information Visualization (IV 2003), pp 169–175

    Google Scholar 

  3. Altschul SF, Erickson BW (1986) Optimal sequence alignment using affine gap costs. Bull Math Biol 48(5–6):603–616

    Article  MATH  MathSciNet  Google Scholar 

  4. Church KW, Helfman JI (1993) Dotplot: a program for exploring self-similarity in millions of lines of text and code. J Comput Graph Stat 2:153–174

    Google Scholar 

  5. Da’dara A, Tsai MH, Tao LF, Marx KA, Shoemaker CB, Harn DA, Skelly PJ (2001) Schistosoma mansoni: molecular characterization of a tegumental Ca-ATPase (SMA3). Exp Parasitol 98(4):215–222

    Google Scholar 

  6. Hamori E, Ruskin J (1983) H curves, a novel method of representation of nucleotide series especially suited for long DNA sequences. J Biol Chem 258(2):1318–1327

    Google Scholar 

  7. Needleman SB, Wunsch CD (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 48(3):443–453

    Google Scholar 

  8. Shibayama G, Imai H (1993) Finding K-best alignments of multiple sequences. In: Proceedings of genome informatics workshop IV, pp 120–129

    Google Scholar 

  9. Smith TF, Waterman MS (1981) Identification of common molecular subsequences. J Mol Biol 147(1):195–197

    Google Scholar 

  10. Stevens R, Goble C, Baker P, Brass A (2001) A classification of tasks in bioinformatics. Bioinformatics 17(2):180–188

    Google Scholar 

  11. Wu D, Roberge J, Cork DJ, Nguyen BG, Grace T (1993) Computer visualization of long genomic sequences. In: IEEE Conference on Visualization (Visualization '93), pp 308–315

    Google Scholar 

  12. Xiao X, Shao S, Ding Y, Huang Z, Chen X, Chou KC (2005) Using cellular automata to generate image representation for biological sequences. Amino Acids 28(1):29–35

    Google Scholar 

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Acknowledgments

The authors would like to thank the reviewers for their valuable suggestions and comments that are helpful to improve the content and quality for this paper. This paper is supported by the Taichung Veterans General Hospital/National Chung Hsing University Joint Research Program, under the contract of TCVGH-NCHU1017613 and TCVGH-NCHU1027619 and the National Science Council of Taiwan, ROC, under the contract of NSC 100-2221-E-005-089- and NSC 101-2221-E-005-093-.

Author information

Authors and Affiliations

  1. Department of Management Information Systems, Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan, ROC

    Meng-Hsiun Tsai

  2. Department of Computer Science and Information Engineering, Hungkuang University, Taichung, Taiwan, ROC

    Hsin-Lung Wang

  3. Microprogram Information Co., LTD., Taichung, Taiwan, ROC

    Teng-Yen Wu

  4. Department of Information Management, National Taichung University of Science and Technology, Taichung, Taiwan, ROC

    Mu-Yen Chen

Authors
  1. Meng-Hsiun Tsai
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  2. Hsin-Lung Wang
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  3. Teng-Yen Wu
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  4. Mu-Yen Chen
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Corresponding author

Correspondence to Mu-Yen Chen .

Editor information

Editors and Affiliations

  1. Seoul National University of Science and Technology (SeoulTech), Seoul, Korea, Republic of (South Korea)

    James J. (Jong Hyuk) Park

  2. Department of Computer Science, Georgia State University, Atlanta, Georgia, USA

    Yi Pan

  3. Digital Media Engineering, Anyang University, Anyang, Korea, Republic of (South Korea)

    Cheonshik Kim

  4. Information & Communication Technologies, Swinburne University of Technology, Melbourne, Victoria, Australia

    Yun Yang

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© 2015 Springer Science+Business Media Dordrecht

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Tsai, MH., Wang, HL., Wu, TY., Chen, MY. (2015). A New RFID and Cellular Automata Based Genetic Sequence Converter. In: Park, J., Pan, Y., Kim, C., Yang, Y. (eds) Future Information Technology - II. Lecture Notes in Electrical Engineering, vol 329. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9558-6_3

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  • DOI: https://doi.org/10.1007/978-94-017-9558-6_3

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-017-9557-9

  • Online ISBN: 978-94-017-9558-6

  • eBook Packages: EngineeringEngineering (R0)

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