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Spectral–Statistical Approach for Revealing Latent Regular Structures in DNA Sequence

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Data Mining Techniques for the Life Sciences

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1415))

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Abstract

Methods of the spectral–statistical approach (2S-approach) for revealing latent periodicity in DNA sequences are described. The results of data analysis in the HeteroGenome database which collects the sequences similar to approximate tandem repeats in the genomes of model organisms are adduced. In consequence of further developing of the spectral–statistical approach, the techniques for recognizing latent profile periodicity are considered. These techniques are basing on extension of the notion of approximate tandem repeat. Examples of correlation of latent profile periodicity revealed in the CDSs with structural–functional properties in the proteins are given.

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Author information

Authors and Affiliations

  1. Institute of Mathematical Problems of Biology, Russian Academy of Sciences, Institutskaya st., 4, 142290, Pushchino, Russia

    Maria Chaley

  2. Department of Computational Mathematics and Mathematical Physics, Moscow State Technical University, n.a. N.E. Bauman the 2nd Baumanskaya st., 5, 105005, Moscow, Russia

    Vladimir Kutyrkin

Authors
  1. Maria Chaley
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  2. Vladimir Kutyrkin
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Corresponding author

Correspondence to Maria Chaley .

Editor information

Editors and Affiliations

  1. Max F. Perutz Laboratories GmbH, Universität Wien, Wien, Austria

    Oliviero Carugo

  2. Technology and Research (A*STAR), Agency for Science, Singapore, Singapore

    Frank Eisenhaber

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Chaley, M., Kutyrkin, V. (2016). Spectral–Statistical Approach for Revealing Latent Regular Structures in DNA Sequence. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 1415. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3572-7_16

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  • DOI: https://doi.org/10.1007/978-1-4939-3572-7_16

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-3570-3

  • Online ISBN: 978-1-4939-3572-7

  • eBook Packages: Springer Protocols

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