IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v414y2014icp216-226.html
   My bibliography  Save this article

A genome signature derived from the interplay of word frequencies and symbol correlations

Author

Listed:
  • Möller, Simon
  • Hameister, Heike
  • Hütt, Marc-Thorsten

Abstract

Genome signatures are statistical properties of DNA sequences that provide information on the underlying species. It is not understood, how such species-discriminating statistical properties arise from processes of genome evolution and from functional properties of the DNA. Investigating the interplay of different genome signatures can contribute to this understanding. Here we analyze the statistical dependences of two such genome signatures: word frequencies and symbol correlations at short and intermediate distances.

Suggested Citation

  • Möller, Simon & Hameister, Heike & Hütt, Marc-Thorsten, 2014. "A genome signature derived from the interplay of word frequencies and symbol correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 216-226.
  • Handle: RePEc:eee:phsmap:v:414:y:2014:i:c:p:216-226
    DOI: 10.1016/j.physa.2014.07.048
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437114006207
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2014.07.048?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tanja Woyke & Hanno Teeling & Natalia N. Ivanova & Marcel Huntemann & Michael Richter & Frank Oliver Gloeckner & Dario Boffelli & Iain J. Anderson & Kerrie W. Barry & Harris J. Shapiro & Ernest Szeto , 2006. "Symbiosis insights through metagenomic analysis of a microbial consortium," Nature, Nature, vol. 443(7114), pages 950-955, October.
    2. Jörg Reichardt & Roberto Alamino & David Saad, 2011. "The Interplay between Microscopic and Mesoscopic Structures in Complex Networks," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-8, August.
    3. P. A. Jacobs & P. A. W. Lewis, 1983. "Stationary Discrete Autoregressive‐Moving Average Time Series Generated By Mixtures," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(1), pages 19-36, January.
    4. Stanley, H.E & Buldyrev, S.V & Goldberger, A.L & Havlin, S & Peng, C.-K & Simons, M, 1999. "Scaling features of noncoding DNA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 273(1), pages 1-18.
    5. Dehnert, M. & Helm, W.E. & Hütt, M.-Th., 2003. "A discrete autoregressive process as a model for short-range correlations in DNA sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 327(3), pages 535-553.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kosmidis, Kosmas & Hütt, Marc-Thorsten, 2023. "DNA visibility graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    2. Xie, Xian-Hua & Yu, Zu-Guo & Ma, Yuan-Lin & Han, Guo-Sheng & Anh, Vo, 2017. "A novel genome signature based on inter-nucleotide distances profiles for visualization of metagenomic data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 87-94.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kosmidis, Kosmas & Hütt, Marc-Thorsten, 2023. "DNA visibility graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    2. A. M. M. Shahiduzzaman Quoreshi & Reaz Uddin & Naushad Mamode Khan, 2019. "Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data—Under Conditional Heteroskedasticity Framework," JRFM, MDPI, vol. 12(2), pages 1-13, April.
    3. Marc A. Scott & Kaushik Mohan & Jacques‐Antoine Gauthier, 2020. "Model‐based clustering and analysis of life history data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1231-1251, June.
    4. Atanu Biswas & Maria Carmen Pardo & Apratim Guha, 2014. "Auto-association measures for stationary time series of categorical data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 487-514, September.
    5. Song‐Hee Kim & Ward Whitt, 2014. "Choosing arrival process models for service systems: Tests of a nonhomogeneous Poisson process," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(1), pages 66-90, February.
    6. Damian Eduardo Taranto & Giacomo Bormetti & Fabrizio Lillo, 2014. "The adaptive nature of liquidity taking in limit order books," Papers 1403.0842, arXiv.org, revised Apr 2014.
    7. Guodong Pang & Ward Whitt, 2012. "The Impact of Dependent Service Times on Large-Scale Service Systems," Manufacturing & Service Operations Management, INFORMS, vol. 14(2), pages 262-278, April.
    8. Maria Eduarda Da Silva & Vera Lúcia Oliveira, 2004. "Difference Equations for the Higher‐Order Moments and Cumulants of the INAR(1) Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(3), pages 317-333, May.
    9. Jarrod J Scott & John A Breier & George W Luther III & David Emerson, 2015. "Microbial Iron Mats at the Mid-Atlantic Ridge and Evidence that Zetaproteobacteria May Be Restricted to Iron-Oxidizing Marine Systems," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-19, March.
    10. Pavlos, G.P. & Karakatsanis, L.P. & Iliopoulos, A.C. & Pavlos, E.G. & Xenakis, M.N. & Clark, Peter & Duke, Jamie & Monos, D.S., 2015. "Measuring complexity, nonextensivity and chaos in the DNA sequence of the Major Histocompatibility Complex," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 188-209.
    11. Kharin, Yuriy & Voloshko, Valeriy, 2021. "Robust estimation for Binomial conditionally nonlinear autoregressive time series based on multivariate conditional frequencies," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    12. Saha, Debajyoti & Shaw, Pankaj Kumar & Ghosh, Sabuj & Janaki, M.S. & Sekar Iyengar, A.N., 2018. "Quantification of scaling exponent with Crossover type phenomena for different types of forcing in DC glow discharge plasma," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 300-310.
    13. Santos, J.V.C. & Moreira, D.M. & Moret, M.A. & Nascimento, E.G.S., 2019. "Analysis of long-range correlations of wind speed in different regions of Bahia and the Abrolhos Archipelago, Brazil," Energy, Elsevier, vol. 167(C), pages 680-687.
    14. Dehnert, M. & Helm, W.E. & Hütt, M.-Th., 2003. "A discrete autoregressive process as a model for short-range correlations in DNA sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 327(3), pages 535-553.
    15. Kumar P Mainali & Sharon Bewick & Peter Thielen & Thomas Mehoke & Florian P Breitwieser & Shishir Paudel & Arjun Adhikari & Joshua Wolfe & Eric V Slud & David Karig & William F Fagan, 2017. "Statistical analysis of co-occurrence patterns in microbial presence-absence datasets," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-21, November.
    16. Silva, R. & Silva, J.R.P. & Anselmo, D.H.A.L. & Alcaniz, J.S. & da Silva, W.J.C. & Costa, M.O., 2020. "An alternative description of power law correlations in DNA sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    17. Lopes, S.R.C. & Nunes, M.A., 2006. "Long memory analysis in DNA sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 361(2), pages 569-588.
    18. Christian H. Weiß, 2018. "Goodness-of-fit testing of a count time series’ marginal distribution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(6), pages 619-651, August.
    19. Christopher S. Withers & Saralees Nadarajah & Shou Hsing Shih, 2015. "Moments and Cumulants of a Mixture," Methodology and Computing in Applied Probability, Springer, vol. 17(3), pages 541-564, September.
    20. Haijia Shi & Lei Shi, 2014. "Identifying Emerging Motif in Growing Networks," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-12, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:414:y:2014:i:c:p:216-226. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.