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On the Approximation of the Kolmogorov Complexity for DNA Sequences

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Pattern Recognition and Image Analysis (IbPRIA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10255))

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Abstract

The Kolmogorov complexity furnishes several ways for studying different natural processes that can be expressed using sequences of symbols from a finite alphabet, such as the case of DNA sequences. Although the Kolmogorov complexity is not algorithmically computable, it can be approximated by lossless normal compressors. In this paper, we use a specific DNA compressor to approximate the Kolmogorov complexity and we assess it regarding its normality. Then, we use it on several datasets, that are constituted by different DNA sequences, representing complete genomes of different species and domains. We show several evolution-related insights associated with the complexity, namely that, globally, archaea have higher relative complexity than bacteria and eukaryotes.

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Acknowledgments

This work was partially funded by FEDER (Programa Operacional Factores de Competitividade - COMPETE) and by National Funds through the FCT - Foundation for Science and Technology, in the context of the projects UID/CEC/00127/2013, PTCD/EEI-SII/6608/2014.

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Authors and Affiliations

  1. IEETA, University of Aveiro, 3810-193, Aveiro, Portugal

    Diogo Pratas & Armando J. Pinho

Authors
  1. Diogo Pratas
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  2. Armando J. Pinho
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Corresponding author

Correspondence to Diogo Pratas .

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Editors and Affiliations

  1. Universidade da Beira Interior , Covilhã, Portugal

    Luís A. Alexandre

  2. University Jaume I , Castellón, Spain

    José Salvador Sánchez

  3. University of the Algarve , Faro, Portugal

    João M. F. Rodrigues

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Pratas, D., Pinho, A.J. (2017). On the Approximation of the Kolmogorov Complexity for DNA Sequences. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_29

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  • DOI: https://doi.org/10.1007/978-3-319-58838-4_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58837-7

  • Online ISBN: 978-3-319-58838-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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