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PRMACA: A Promoter Region Identification Using Multiple Attractor Cellular Automata (MACA)

  • Conference paper
ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol I

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 248))

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Abstract

Promoter region identification from sequences of DNA has gained a remarkable attention in recent years. Even though there are some identification techniques addressing this problem, the approximate accuracy in identifying the promoter region is closely 70% to 72%. An automated procedure was evolved with MACA (Multiple Attractor Cellular Automata) for identifying promoter regions. We have tested the proposed classifier ENCODE benchmark datasets with over three dozens of modern competing predictors shows that proposed algorithm (PRMACA) provides the best overall accuracy that ranges between 77% and 88.7%. PRMACA can identify promoter region with DNA or Amino acid sequences as inputs.

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References

  1. Sandvej, K., Gratama, J.W., Munch, M., Zhou, X.-G., Bolhuis, R.L., Andresen, B.S., Gregersen, N., Hamilton-Dutoit, S.: Sequence analysis of the Epstein-Barr virus (EBV) latent membrane protein-1 gene and promoter region: identification of four variants among wild-type EBV isolates. Blood 90(1), 323–330 (1997)

    Google Scholar 

  2. Lavrovsky, Y., Schwartzman, M.L., Levere, R.D., Kappas, A., Abraham, N.G.: Identification of binding sites for transcription factors NF-kappa B and AP-2 in the promoter region of the human heme oxygenase 1 gene. In: Proceedings of the National Academy of Sciences, vol. 91(13), pp. 5987–5991 (1994)

    Google Scholar 

  3. Horikawa, I., LouAnn Cable, P., Afshari, C., Carl Barrett, J.: Cloning and characterization of the promoter region of human telomerase reverse transcriptase gene. Cancer Research 59(4), 826–830 (1999)

    Google Scholar 

  4. Miskimins, W.K., Roberts, M.P., McClelland, A., Ruddle, F.H.: Use of a protein-blotting procedure and a specific DNA probe to identify nuclear proteins that recognize the promoter region of the transferrin receptor gene. In: Proceedings of the National Academy of Sciences, vol. 82(20), pp. 6741–6744 (1985)

    Google Scholar 

  5. Huang, Q.R., Morris, D., Manolios, N.: Identification and characterisation of polymorphisms in the promoter region of the human Apo-1/Fas (CD95) gene. Molecular Immunology 34(8), 577–582 (1997)

    Article  Google Scholar 

  6. Okuyama, Y., Ishiguro, H., Nankai, M., Shibuya, H., Watanabe, A., Arinami, T.: Identification of a polymorphism in the promoter region of DRD4 associated with the human novelty seeking personality trait. Molecular Psychiatry 5(1), 64–69 (2004)

    Article  Google Scholar 

  7. Bauer, C.E., Young, D.A., Marrs, B.L.: Analysis of the Rhodobacter capsulatus puf operon. Location of the oxygen-regulated promoter region and the identification of an additional puf-encoded gene. Journal of Biological Chemistry 263(10), 4820–4827 (1988)

    Google Scholar 

  8. Mitra, D., Smith, M.: Digital Sequences Processing in Promoter Region Identification. In: Innovations in Applied Artificial Intelligence. Lecture Notes in Computer Science, vol. 3029, pp. 40–49 (2004)

    Google Scholar 

  9. Reese, M.G.: Application of a time-delay neural network to promoter annotation in the Drosophila melanogaster genome. Comput. Chem. 26(1), 51–56 (2001)

    Article  Google Scholar 

  10. Abagyan, R., Batalov, S., Cardozo, T., Totrov, M., Webber, J., Zhou, Y.: Homology Modeling With Internal Coordinate Mechanics: Deformation Zone Mapping and Improvements of Models via Conformational Search. Promoters: Region, Function and Genetics 1, 29–37 (1997)

    Article  Google Scholar 

  11. Maji, P., Pal Chaudhuri, P.: FMACA: A Fuzzy Cellular Automata Based Pattern Classifier. In: Lee, Y., Li, J., Whang, K.-Y., Lee, D. (eds.) DASFAA 2004. LNCS, vol. 2973, pp. 494–505. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Kiran Sree, P., Babu, I.R.: Investigating an Artificial Immune System to Strengthen the Promoter Region Identification and Promoter Coding Region Identification using Cellular Automata Classifier. International Journal of Bioinformatics Research and Applications 5(6), 647–662 (2009)

    Article  Google Scholar 

  13. Kiran Sree, P., Babu, I.R.: Identification of Promoter Region in Genomic DNA Using Cellular Automata Based Text Clustering. The International Arab Journal of Information Technology (IAJIT) 7(1), 75–78 (2010)

    Google Scholar 

  14. Kiran Sree, P., Babu, I.R.: A Novel Promoter Coding Region Identifying Tool using Cellular Automata Classifier with Trust-Region Method and Parallel Scan Algorithm (NPCRITCACA). International Journal of Biotechnology & Biochemistry (IJBB) 4(2), 177-189 (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Jawaharlal Nehru Technological Universtiy Hyderabad, Kukatpally, India

    Pokkuluri Kiran Sree

  2. Department of Computer Science & Engineering, Acharya Nagarjuna University, Guntur, India

    Inampudi Ramesh Babu

  3. Dept of CSE, Jawaharlal Nehru Technological Universtiy, Kakinada, India

    S. S. S. N. Usha Devi Nedunuri

Authors
  1. Pokkuluri Kiran Sree
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  2. Inampudi Ramesh Babu
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  3. S. S. S. N. Usha Devi Nedunuri
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Corresponding author

Correspondence to Pokkuluri Kiran Sree .

Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India

    Suresh Chandra Satapathy

  2. College of Engineering(A), Andhra University, Vishakapatnam, India

    P. S. Avadhani

  3. University of Hyderabad, Hyderabad, India

    Siba K. Udgata

  4. CSIR-National Institute of Oceanography, Visakhapatnam, India

    Sadasivuni Lakshminarayana

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Sree, P.K., Babu, I.R., Nedunuri, S.S.S.N.U.D. (2014). PRMACA: A Promoter Region Identification Using Multiple Attractor Cellular Automata (MACA). In: Satapathy, S., Avadhani, P., Udgata, S., Lakshminarayana, S. (eds) ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol I. Advances in Intelligent Systems and Computing, vol 248. Springer, Cham. https://doi.org/10.1007/978-3-319-03107-1_42

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  • DOI: https://doi.org/10.1007/978-3-319-03107-1_42

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03106-4

  • Online ISBN: 978-3-319-03107-1

  • eBook Packages: EngineeringEngineering (R0)

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