Emergence of new structure from non-stationary analysis of genomic sequences

Nidhal Bouaynaya, Dan Schonfeld

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Scopus citations

    Abstract

    In this paper, we will bring to bear new tools to analyze non-stationary signals that have emerged in the statistical and signal processing community over the past few years. The emergence of these new methods will be used to shed new light and help resolve the issues of (i) the existence of long-range correlations in DNA sequences and (ii) whether they are present in both coding and non-coding segments or only in the latter. It turns out that the statistical differences between coding and non-coding segments are much more subtle than previously thought using stationary analysis. In particular, both coding and non-coding sequences exhibit long-range correlations, as asserted by a 1/f β(n) evolutionary (i.e., time-dependent) spectrum. However, we will use an index of randomness, which we derive from the Hilbert-Huang Transform, to demonstrate that coding sequences, although not random as previously suspected, are often "more random" (i.e., more white) than non-coding sequences. Moreover, the study of the evolution of the rate of change of these time-dependent parameters in homologous gene families shows a sudden jump around the rat, which might be related to the well-known supercharged evolution of this rodent.

    Original languageEnglish (US)
    Title of host publicationGENSIPS'08 - 6th IEEE International Workshop on Genomic Signal Processing and Statistics
    DOIs
    StatePublished - Sep 17 2008
    Event6th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'08 - Phoenix, AZ, United States
    Duration: Jun 8 2008Jun 10 2008

    Publication series

    NameGENSIPS'08 - 6th IEEE International Workshop on Genomic Signal Processing and Statistics

    Other

    Other6th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'08
    CountryUnited States
    CityPhoenix, AZ
    Period6/8/086/10/08

    All Science Journal Classification (ASJC) codes

    • Genetics
    • Signal Processing
    • Electrical and Electronic Engineering
    • Statistics, Probability and Uncertainty

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